From efaa12660960261ac31c8359d763080914d4fdb7 Mon Sep 17 00:00:00 2001 From: Jeffrey Ma Date: Mon, 30 Oct 2023 15:36:43 -0400 Subject: [PATCH 01/12] added figure refs --- about.html | 735 ++++++ acknowledgements.html | 673 ++++++ contributors.html | 714 ++++++ copyright.html | 672 ++++++ dedication.html | 668 ++++++ embedded_sys.html | 1231 ++++++++++ front.html | 663 ++++++ index.html | 722 ++++++ introduction.html | 735 ++++++ optimizations.qmd | 13 +- references.bib | 6 + site_libs/bootstrap/bootstrap-icons.css | 2018 +++++++++++++++++ site_libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes site_libs/bootstrap/bootstrap.min.css | 10 + site_libs/bootstrap/bootstrap.min.js | 7 + site_libs/clipboard/clipboard.min.js | 7 + site_libs/quarto-html/anchor.min.js | 9 + site_libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 203 ++ site_libs/quarto-html/quarto.js | 902 ++++++++ site_libs/quarto-html/tippy.css | 1 + site_libs/quarto-html/tippy.umd.min.js | 2 + site_libs/quarto-nav/quarto-nav.js | 277 +++ site_libs/quarto-search/autocomplete.umd.js | 3 + site_libs/quarto-search/fuse.min.js | 9 + site_libs/quarto-search/quarto-search.js | 1140 ++++++++++ 26 files changed, 11420 insertions(+), 6 deletions(-) create mode 100644 about.html create mode 100644 acknowledgements.html create mode 100644 contributors.html create mode 100644 copyright.html create mode 100644 dedication.html create mode 100644 embedded_sys.html create mode 100644 front.html create mode 100644 index.html create mode 100644 introduction.html create mode 100644 site_libs/bootstrap/bootstrap-icons.css create mode 100644 site_libs/bootstrap/bootstrap-icons.woff create mode 100644 site_libs/bootstrap/bootstrap.min.css create mode 100644 site_libs/bootstrap/bootstrap.min.js create mode 100644 site_libs/clipboard/clipboard.min.js create mode 100644 site_libs/quarto-html/anchor.min.js create mode 100644 site_libs/quarto-html/popper.min.js create mode 100644 site_libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 site_libs/quarto-html/quarto.js create mode 100644 site_libs/quarto-html/tippy.css create mode 100644 site_libs/quarto-html/tippy.umd.min.js create mode 100644 site_libs/quarto-nav/quarto-nav.js create mode 100644 site_libs/quarto-search/autocomplete.umd.js create mode 100644 site_libs/quarto-search/fuse.min.js create mode 100644 site_libs/quarto-search/quarto-search.js diff --git a/about.html b/about.html new file mode 100644 index 00000000..b9fc4508 --- /dev/null +++ b/about.html @@ -0,0 +1,735 @@ + + + + + + + + + + +MACHINE LEARNING SYSTEMS - About the Book + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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About the Book

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Overview

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Welcome to this collaborative project initiated by the CS249r Tiny Machine Learning class at Harvard University. Our goal is to make this book a community resource that assists educators and learners in understanding TinyML. The book will be regularly updated to reflect new insights into TinyML and effective teaching methods.

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Topics Explored

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This book offers a comprehensive look at various aspects of embedded machine learning. The topics we delve into include:

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  • Introduction and Overview of Embedded Machine Learning
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  • Data Engineering Techniques
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  • Frameworks for Embedded Machine Learning
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  • Efficient Representation and Compression of Models
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  • Performance Metrics and Benchmarking for Machine Learning Systems
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  • Edge Learning
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  • Hardware Acceleration Options: GPUs, TPUs, and FPGAs
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  • Operational Aspects of Embedded Machine Learning
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  • Security and Privacy in On-Device Machine Learning
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  • Ethical Considerations in AI
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  • Sustainability Concerns in Edge Computing
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  • Generative AI in Edge Computing
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By the time you finish this book, you’ll have a foundational understanding of machine learning and the Internet of Things. You’ll also learn about real-world applications of embedded machine learning systems and gain practical experience through project-based assignments.

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Who Should Read This

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This book is tailored for those new to the exciting field of tiny machine learning (TinyML). It starts with the basics of machine learning and embedded systems and progresses to more advanced topics relevant to the TinyML community and broader research areas. The book is particularly beneficial for:

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  • Embedded Systems Engineers: For engineers in the embedded systems domain, this book serves as an excellent guide to TinyML, helping them create intelligent applications on resource-limited platforms.

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  • Students in Computer Science and Electrical Engineering: This book is a useful resource for students studying computer science and electrical engineering. It introduces them to the methods, algorithms, and techniques used in TinyML, preparing them for real-world challenges in embedded machine learning.

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  • Researchers and Academics: Those involved in machine learning, computer vision, and signal processing research will find this book insightful. It sheds light on the unique challenges of running machine learning algorithms on low-power, low-memory devices.

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  • Industry Professionals: If you’re working in areas like IoT, robotics, wearable tech, or smart devices, this book will equip you with the knowledge you need to add machine learning features to your products.

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Key Learning Outcomes

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Readers will acquire skills in training and deploying deep neural network models on resource-limited microcontrollers, along with understanding the broader challenges involved in their design, development, and deployment. Specifically, you’ll learn about:

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  • Foundational Concepts in Machine Learning
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  • Fundamentals of Embedded AI
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  • Hardware Platforms Suitable for Embedded AI
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  • Techniques for Training Models for Embedded Systems
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  • Strategies for Model Optimization
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  • Real-world Applications of Embedded AI
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  • Current Challenges and Future Trends in Embedded AI
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Our aim is to make this book a comprehensive resource for anyone interested in developing intelligent applications on embedded systems. Upon completing the book, you’ll be well-equipped to design and implement your own machine learning-enabled projects.

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Prerequisites for Readers

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  • Basic Programming Skills: We recommend that you have some prior programming experience, ideally in Python. A grasp of variables, data types, and control structures will make it easier to engage with the book.

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  • Some Machine Learning Knowledge: While not mandatory, a basic understanding of machine learning concepts will help you absorb the material more readily. If you’re new to the field, the book provides enough background information to get you up to speed.

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  • Python Programming (Optional): If you’re familiar with Python, you’ll find it easier to engage with the coding sections of the book. Knowing libraries like NumPy, scikit-learn, and TensorFlow will be particularly helpful.

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  • Willingness to Learn: The book is designed to be accessible to a broad audience, with varying levels of technical expertise. A willingness to challenge yourself and engage in practical exercises will help you get the most out of it.

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  • Resource Availability: For the hands-on aspects, you’ll need a computer with Python and the relevant libraries installed. Optional access to an embedded development board or microcontroller will also be beneficial for experimenting with machine learning model deployment.

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By meeting these prerequisites, you’ll be well-positioned to deepen your understanding of TinyML, engage in coding exercises, and even implement practical applications on embedded devices.

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Acknowledgements

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Assembling this book has been an incredible journey, spanning several years of hard work. The initial idea for this book sprang from the tinyML edX course, and its realization would not have been possible without the invaluable contributions of countless individuals. We are deeply indebted to the researchers whose groundbreaking work laid the foundation for this book.

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We extend our heartfelt gratitude to the GitHub community. Whether you contributed an entire section, a single sentence, or merely corrected a typo, your efforts have significantly enhanced this book. We deeply appreciate everyone’s time, expertise, and commitment. This book is as much yours as it is ours.

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Special thanks go to Professor Vijay Janapa Reddi, whose belief in the transformative power of open-source communities and invaluable guidance have been our guiding light from the outset.

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We also owe a great deal to the team at GitHub. You’ve revolutionized the way people collaborate, and this book stands as a testament to what can be achieved when barriers to global cooperation are removed.

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To all who pick up this book—thank you! We wrote it with you in mind, hoping to provoke thought, inspire questions, and perhaps even ignite a spark of inspiration. After all, what is the point of writing if no one is reading?

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Last but certainly not least, our deepest thanks go to our friends, families, mentors, and all the kind souls who have supported us emotionally and intellectually as this book came to fruition.

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Contributors

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We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, and time to enhance both the content and codebase of this project. Below you will find a list of all contributors. If you would like to contribute to this project, please see our GitHub page.

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+Vijay Janapa Reddi
Vijay Janapa Reddi
📖 +		 +ishapira
ishapira
📖 +		 +Ikechukwu Uchendu
Ikechukwu Uchendu
📖 +		 +Shvetank Prakash
Shvetank Prakash
📖 +		 +oishib
oishib
📖 +		 +sjohri20
sjohri20
📖 +		 +Marco Zennaro
Marco Zennaro
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+Jessica Quaye
Jessica Quaye
📖 +		 +Marcelo Rovai
Marcelo Rovai
📖 +		 +Matthew Stewart
Matthew Stewart
📖 +
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Copyright

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This book is open-source and developed collaboratively through GitHub. Unless otherwise stated, this work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). You can find the full text of the license here.

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Contributors to this project have dedicated their contributions to the public domain or under the same open license as the original project. While the contributions are collaborative, each contributor retains copyright in their respective contributions.

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For details on authorship, contributions, and how to contribute, please see the project repository on GitHub.

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All trademarks and registered trademarks mentioned in this book are the property of their respective owners.

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The information provided in this book is believed to be accurate and reliable. However, the authors, editors, and publishers cannot be held liable for any damages caused or alleged to be caused either directly or indirectly by the information contained in this book.

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Dedication

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This book is a testament to the idea that, in the vast expanse of technology and innovation, it’s not always the largest systems, but the smallest ones, that can change the world.

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2  Embedded Systems

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In the domain of TinyML, embedded systems serve as the bedrock, providing a robust platform where intelligent algorithms can function both efficiently and effectively. Defined by their specialized roles and real-time computational capabilities, these systems act as the convergence point where data and computation intersect on a micro-scale. Tailored to meet the demands of specific tasks, they excel in optimizing performance, energy usage, and spatial efficiency—key considerations in the successful implementation of TinyML solutions.

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As we journey further into this chapter, we will demystify the intricate yet captivating realm of embedded systems, gaining insights into their structural design, operational features, and the crucial part they play in enabling TinyML applications. From an introduction to the fundamentals of microcontroller units to a deep dive into the interfaces and peripherals that amplify their capabilities, this chapter aims to be a comprehensive guide for understanding the nuanced aspects of embedded systems within the TinyML landscape.

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  • Understand the definition, characteristics, history, and importance of embedded systems, especially in relation to tinyML.

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  • Examine the embedded system architecture including microcontrollers vs microprocessors, memory types and management, and System on Chip (SoC).

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  • Explore embedded system programming including languages like C and Python, firmware development, and real-time operating systems (RTOS).

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  • Discuss interfaces and peripherals like digital/analog I/O, communication protocols, etc.

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  • Analyze power management considerations, energy-efficient design, and battery management.

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  • Understand real-time characteristics of embedded systems including clocks, timing, task scheduling, and error handling.

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  • Evaluate security, reliability and safety-critical aspects of embedded systems.

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  • Identify future trends and challenges like edge computing, scalability, and market opportunities.

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2.1 Basics and Components

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2.1.1 Definition and Characteristics

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Embedded systems are specialized forms of computing that do not resemble traditional computers. These systems are dedicated to particular tasks and integrate as components within larger devices. Unlike general-purpose computers capable of running a multitude of applications, embedded systems are designed to execute predefined tasks, often with stringent requirements. Due to their task-specific nature, their architecture is optimized for performance and reliability. The defining traits of these systems include:

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  1. Dedicated Functionality: These systems are engineered to carry out a specific function or a cluster of closely related functions. This specialization allows for optimization, resulting in enhanced performance and reliability.

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  2. Real-Time Operation: A large number of embedded systems function in real-time, necessitating immediate responses to environmental inputs or changes within a set time frame.

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  3. Integration with Physical Hardware: Unlike general-purpose computing systems, embedded systems are tightly integrated with physical components, making them more mechanically oriented.

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  4. Long Lifecycle: Typically, these systems have an extended lifecycle, continuing to operate for many years post their initial deployment.

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  5. Resource Constraints: Often operating under resource limitations, embedded systems require efficient algorithms and software due to restricted computational power and memory.

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2.1.2 Historical Background

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The lineage of embedded systems dates back to the 1960s, marked by the introduction of the first microprocessor, labeled as Figure fig-Intel4004. This groundbreaking development led to the creation of the inaugural embedded system used in the Apollo Guidance Computer, the primary navigational system for the Apollo spacecraft. Over subsequent years, the domain has expanded remarkably, finding utility in diverse sectors such as automotive electronics, consumer electronics, telecommunications, and healthcare.

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Figure 2.1: Intel 4004.
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2.1.3 Importance in tinyML

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Within the tinyML framework, embedded systems constitute a vital frontier. The direct integration of machine learning algorithms into these systems enables intelligent, edge-based decision-making, thereby minimizing latency and bolstering security. Here are several factors that underscore the importance of embedded systems in the tinyML ecosystem:

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  1. Edge Computing: By localizing computation near the data source, embedded systems amplify efficiency and diminish the need for continuous interaction with centralized data repositories.

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  2. Low Power Consumption: Designed for minimal energy usage, embedded systems in tinyML are particularly suited for battery-dependent devices and Internet of Things applications.

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  3. Real-Time Analysis and Decision Making: These systems can conduct instantaneous data analysis, facilitating immediate decisions based on the generated insights.

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  4. Security and Privacy: Local data processing on embedded systems enhances security and privacy by reducing the likelihood of data interception during transmission.

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  5. Cost-Effective: The deployment of machine learning models on embedded systems can be economically advantageous, particularly when data transmission and cloud storage could incur substantial costs.

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As we progress further into this chapter, we will uncover the complexities that dictate the operations of embedded systems and examine how they serve as the foundational layer upon which tinyML is built, heralding a future filled with integrated, intelligent, and efficient devices and systems.

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2.2 Embedded System Architecture

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The architectural layout of embedded systems serves as the schematic that outlines the structure and operations of these specialized entities. It sheds light on the interactions and collaborations among various components within an embedded system. This section will dissect the key elements of the architecture, including microcontrollers, microprocessors, diverse types of memory and their management, as well as the complexities of System on Chip (SoC).

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2.2.1 Microcontrollers vs Microprocessors

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Comprehending the distinctions between microcontrollers and microprocessors is essential for understanding the basics of embedded system architecture. In this section, we will explore the unique attributes of each:

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  • Microcontrollers

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    Microcontrollers are compact, integrated circuits engineered to control specific functions within an embedded system. They incorporate a processor, memory, and input/output peripherals within a single unit, as depicted in Figure fig-microcontroller, simplifying the overall system design. Microcontrollers are generally employed in applications where computational demands are moderate and cost-effectiveness is a primary consideration.

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    Characteristics:

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    • Single-chip solution
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Figure 2.2: Microcontrollers
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  • Microprocessors

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    In contrast, microprocessors are more intricate and serve as the central processing unit within a system. They lack the integrated memory and input/output peripherals commonly found in microcontrollers. These processors are typically present in systems requiring elevated computational power and adaptability. They are suitable for devices where high processing power is a necessity and the tasks are data-intensive.

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    Characteristics:

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    • Necessitates external components like memory and input/output peripherals
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    • Elevated processing power in comparison to microcontrollers
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    • Greater flexibility for connectivity with diverse components
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    • Well-suited for data-intensive tasks
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2.2.2 Memory Types and Management

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Embedded systems utilize a variety of memory types, each fulfilling specific roles. Efficient memory management is vital for optimizing both performance and resource utilization. The following section elaborates on different types of memory and their management within the context of embedded systems:

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  • ROM (Read-Only Memory): This non-volatile memory retains data written during the manufacturing process and remains unaltered throughout the lifespan of the device. It houses firmware and boot-up instructions.

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  • RAM (Random Access Memory): This volatile memory stores transient data generated during system operation. It is faster and permits read-write operations, but data is lost when power is disconnected.

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  • Flash Memory: This is a type of non-volatile memory that can be electrically erased and reprogrammed. It is commonly used for storing firmware or data that must be retained between system reboots.

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Memory Management:

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  • Static Memory Allocation: In this approach, memory is allocated prior to runtime and remains fixed throughout system operation.

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  • Dynamic Memory Allocation: Here, memory is allocated during runtime, offering flexibility but introducing the risk of increased complexity and potential memory leaks.

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2.2.3 System on Chip (SoC)

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The majority of embedded systems are Systems on Chip (SoCs). An SoC embodies an advanced level of integration technology, incorporating most components required to construct a complete system onto a single chip. It often includes a microprocessor or microcontroller, blocks of memory, peripheral interfaces, and other requisite components for a fully operational system. Below is a detailed examination of its characteristics and applications:

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  • Integration of Multiple Components: SoCs consolidate multiple components like CPUs, memory, and peripherals onto a single chip, facilitating higher levels of integration and reducing the need for external components.

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  • Power Efficiency: The high degree of integration often results in SoCs being more power-efficient compared to systems assembled from separate chips.

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  • Cost-Effectiveness: The integrated nature leads to reduced manufacturing expenses, as fewer individual components are needed.

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  • Applications: SoCs are employed in a diverse range of sectors including mobile computing, automotive electronics, and Internet of Things devices where compact form factors and energy efficiency are highly valued.

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Here is a list of widely recognized SoCs that have found substantial applications across various domains:

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  1. Qualcomm Snapdragon: Predominantly used in smartphones and tablets, these SoCs offer a blend of processing power, graphics, and connectivity features.

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  2. Apple A-series: Custom-developed SoCs by Apple, used in their lineup of iPhones, iPads, and in certain versions of Apple TV and HomePod. Notable examples include the A14 Bionic and A15 Bionic chips.

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  3. Samsung Exynos: Developed by Samsung, these SoCs are extensively used in their range of smartphones, tablets, and other electronic devices.

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  4. NVIDIA Tegra: Initially intended for mobile devices, these SoCs have found significant applications in automotive and gaming consoles, such as the Nintendo Switch. A visual representation can be seen below in Figure fig-soc.

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  5. Intel Atom: Employed in a wide array of systems including netbooks, smartphones, and even embedded systems, these SoCs are known for their power efficiency.

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  6. MediaTek Helio: Commonly found in budget to mid-range smartphones, these chips offer a balanced mix of power efficiency and performance.

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  7. Broadcom SoCs: Extensively used in networking equipment, Broadcom provides a variety of SoCs with diverse functionalities, including those optimized for wireless communications and data processing.

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  8. Texas Instruments (TI) OMAP: Previously popular in smartphones and tablets, these SoCs offered a range of functionalities including multimedia processing and connectivity.

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  9. Xilinx Zynq: Mainly used in embedded systems for industrial automation and in applications requiring high levels of data processing, such as advanced driver-assistance systems (ADAS).

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  10. Altera SoC FPGA: Now a part of Intel, these SoCs combine FPGA technology with ARM cores, offering flexibility and performance for a range of applications including automotive and industrial systems.

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Figure 2.3: NVIDIA’s Tegra 2 combines two ARM Cortex-A9 cores with an ARM7 for SoC management tasks.
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Each of these Systems on Chip (SoCs) offers a unique array of features and capabilities, tailored to meet the diverse demands of an ever-evolving technological landscape. They consolidate multiple components onto a single chip, delivering power efficiency, cost-effectiveness, and compact solutions suitable for contemporary electronic devices.

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2.3 Embedded System Programming

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Programming for embedded systems differs significantly from traditional software development, being specifically designed to navigate the constraints of limited resources and real-time requirements commonly associated with embedded hardware. This section aims to shed light on the distinct programming languages employed, delve into the subtleties of firmware development, and explore the pivotal role of Real-time Operating Systems (RTOS) in this specialized domain.

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2.3.1 Programming Languages: C, C++, Python, etc.

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Choosing the right programming languages is essential in embedded systems, often emphasizing direct hardware interaction and memory usage optimization. Here, we will examine the unique attributes of these languages and how they differ from those commonly used in more conventional computing systems:

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  • C: Often considered the bedrock of embedded systems programming, the C language enables direct engagement with hardware, providing capabilities for bit-wise operations and memory address manipulation. Its procedural nature and low-level functionalities make it the preferred choice for resource-constrained environments, particularly for firmware development.

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  • C++: Building upon the foundational principles of C, C++ incorporates object-oriented features, promoting organized and modular code development. Despite its inherent complexity, it is employed in scenarios where higher-level abstractions do not undermine the detailed control offered by C.

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  • Python: Although not a traditional choice for embedded systems due to its higher memory consumption and runtime delays, Python is gradually gaining traction in the embedded sphere, particularly in systems with less stringent resource limitations. A specialized variant known as MicroPython has been developed, optimized for microcontrollers and retaining the simplicity and ease of Python. This flexible programming paradigm facilitates quick prototyping and development, as illustrated by the code snippet below that interfaces with pins on a PyBoard.

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import pyb # Package from PyBoard
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+# print some text to the serial console
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Comparison with Traditional Systems: In contrast to mainstream computing systems, where languages like Java, Python, or JavaScript are lauded for their ease of development and extensive libraries, embedded systems favor languages that provide fine-grained control over hardware and opportunities for optimization, all while carefully navigating resource constraints.

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2.3.2 Firmware Development

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Firmware development in embedded systems involves creating programs that are permanently stored in the device’s non-volatile memory, ensuring consistent operation. This section outlines how firmware development diverges from software development in traditional computing systems:

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  1. Resource Optimization: The imperative for continual optimization is paramount, enabling the code to operate within the limitations of restricted memory and processing capabilities.

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  2. Hardware Interaction: Firmware often maintains a close relationship with hardware, requiring an in-depth understanding of hardware components and their functionalities.

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  3. Lifecycle Management: Firmware updates are less frequent than software updates in traditional systems, necessitating rigorous testing to prevent failures that could lead to hardware malfunctions.

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  4. Security Concerns: Given its integral role, firmware is a potential target for security breaches, necessitating meticulous attention to security aspects, including secure coding practices and encryption protocols.

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2.3.3 Real-time Operating Systems (RTOS)

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RTOSs serve as the backbone for real-time embedded systems, managing task execution in a predictable and deterministic manner. This is a marked departure from operating systems in general-purpose computing, as outlined below:

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  1. Deterministic Timing: RTOSs are designed to respond to inputs or events within a well-defined time frame, fulfilling the stringent time-sensitive requirements of many embedded systems.

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  2. Task Prioritization: These systems enable task prioritization, allowing critical tasks to receive preferential processing time over less crucial tasks.

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  3. Microkernel Architecture: Many RTOSs employ a microkernel architecture, epitomizing efficiency and minimalism by focusing solely on essential functionalities.

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  4. Memory Management: Memory management in RTOSs is often more streamlined compared to their counterparts in traditional operating systems, contributing to quick response times and operational efficiency.

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Examples of RTOS: Notable instances in this category include FreeRTOS, RTEMS, and VxWorks, each providing unique features tailored to meet the varied needs of different embedded systems applications.

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2.4 Interfaces and Peripherals

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Embedded systems engage with the external environment through a range of interfaces and peripherals, which are often more specialized and streamlined than those in general-purpose systems. Let us explore these in detail:

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2.4.1 Digital I/O

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Digital Input/Output (I/O) interfaces are fundamental to embedded systems, enabling interaction with other devices and components. For instance, a digital I/O pin may be used to read a binary signal (0 or 1) from sensors or to control actuators. In embedded systems, these I/O ports often operate under strict timing constraints, a

+

requirement less common in general-purpose computing systems. Moreover, these systems are usually programmed for specific, optimized operations on digital signals, sometimes needing to function in real-time or near-real-time settings.

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2.4.2 Analog Interfaces

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Analog interfaces in embedded systems are vital for interacting with a predominantly analog world. These interfaces may include components like Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). For example, ADCs can be employed to read sensor data from environmental sensors such as temperature or humidity sensors, converting real-world analog data into a digital format that the microcontroller can process.

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In contrast to general-purpose systems, embedded systems often utilize analog interfaces more directly and frequently, especially in sensor-integrated applications that require the conversion of a broad range of analog signals into digital data for further processing and analysis.

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If you examine Figure fig-nicla-io closely, you will notice indications of I/O pinouts for analog, digital, and communication layouts.

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Figure 2.4: Nicla Vision pinout
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2.4.3 Communication Protocols (SPI, I2C, UART, etc.)

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Communication protocols act as the channels that enable communication between various components within or connected to an embedded system. Let us examine some commonly used ones:

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  • SPI (Serial Peripheral Interface): This synchronous serial communication protocol is primarily used for short-distance communication in embedded systems. For instance, it is frequently employed in communications with SD cards and TFT displays.

    • +

  • I2C (Inter-Integrated Circuit): This multi-master, multi-slave, packet-switched, single-ended, serial communication bus is widely used in embedded systems to connect low-speed peripherals to motherboards, embedded systems, or cell phones. It is valued for its simplicity and low pin count.

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  • UART (Universal Asynchronous Receiver-Transmitter): This protocol enables asynchronous serial communication between devices. It is commonly used in embedded systems to transmit data between devices over a serial port, such as sending data logs from a sensor node to a computer.

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Compared to general-purpose systems, communication protocols in embedded systems are often more finely tuned for speed and reliability, especially in critical applications where data transmission integrity is crucial. Additionally, these protocols may be directly integrated into the microcontroller, facilitating more cohesive and seamless interactions between components, a feature less commonly observed in general-purpose systems.

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2.5 Power Management in Embedded Systems

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Power management is a critical focus area in the design of embedded systems, influencing both the system’s efficiency and its applicability in real-world scenarios. The wide range of applications for embedded systems, from handheld devices to industrial equipment, highlights the need for meticulous power management. Let us explore this essential aspect of embedded systems:

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2.5.1 Power Consumption Considerations

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In embedded systems, power consumption is a key factor that dictates both performance and longevity. Microcontrollers in these systems usually operate within a voltage range of 1.8V to 5V, with current consumption varying from microamperes (μA) to milliamperes (mA) during active states. In sleep or standby modes, the current consumption can drop to nanoamperes (nA), extending battery life.

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In contrast, general-purpose computing systems like desktop computers consume power on the scale of tens to hundreds of watts, several orders of magnitude higher than embedded systems. This significant difference underscores the need for careful power management in embedded systems, where the power budget is often much more limited.

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Managing power consumption involves a complex interplay of factors such as operating voltage, clock frequency, and the specific tasks the system performs. Engineers often find themselves balancing power consumption against performance and responsiveness, navigating a complex landscape of trade-offs.

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2.5.2 Energy-Efficient Design

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Incorporating energy efficiency into the design phase is crucial for the successful deployment of embedded systems. Techniques like dynamic voltage and frequency scaling (DVFS) are often employed, allowing the system to adjust voltage and frequency dynamically based on processing needs, thereby optimizing power consumption.

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Additionally, the use of low-power modes, where non-essential peripherals are deactivated or clock frequencies are reduced, can significantly conserve energy. For example, deep sleep modes that consume as little as 100 nA can dramatically extend battery life, particularly in battery-operated embedded systems.

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The architecture of the microcontroller, especially its instruction set architecture (ISA), is often highly specialized to eliminate unnecessary complexities that could increase power consumption. This specialization allows operations to be executed in fewer cycles compared to general-purpose processors, reducing the power consumed per operation. Moreover, these specialized ISAs are designed to efficiently execute the specific tasks that the embedded system is intended to perform, optimizing the execution path and thereby saving energy.

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2.5.3 Battery Management

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Managing batteries is an integral component of power management strategies in embedded systems. The goal is to maximize battery life without sacrificing performance. Battery-powered embedded systems often use lithium-ion or lithium-polymer batteries due to their high energy density and rechargeable features. These batteries typically have a voltage range of 3.7V to 4.2V per cell. For example, the Nicla Vision utilizes a 3.7V battery, as shown in Figure fig-battery.

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Figure 2.5: Nicla Vision battery
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By focusing on these elements, engineers can create systems that not only meet functional requirements but do so in a manner that reflects a deep understanding of the broader impacts of technology on society and the environment.

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Engineers are tasked with implementing methods such as effective charge regulation, protection against voltage spikes, and thermal monitoring to ensure the longevity and health of the battery. Additionally, the incorporation of systems that can tap into renewable energy sources like solar or kinetic energy can augment battery reserves, leading to enduring and eco-friendly solutions.

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The emphasis on power management is driven by the imperative to make the most of available resources, prolong battery longevity, and minimize operational expenditures. In scenarios where the embedded systems are situated in remote or hard-to-reach locations, adept power management can substantially cut down on the frequency of maintenance visits, thereby guaranteeing continuous and seamless functionality.

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It’s fair to assert that power management goes beyond being a mere technical specification in embedded systems; it serves as a pivotal factor that can either make or break the success of a project. Significant engineering effort is channeled into fine-tuning power management approaches, aiming to develop systems that are not just operationally efficient but also environmentally sustainable. This reflects a profound dedication to both technological innovation and excellence within the realm of embedded systems.

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2.6 Real-Time Characteristics

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Within the complex tapestry of embedded systems, real-time attributes serve as essential threads, interlacing various components and tasks into a unified, responsive whole. This element, often specific to embedded systems, occupies a vital role in both their architecture and functionality, endowing them with the nimbleness and accuracy needed for timely interaction with their surroundings. Let’s examine the nuances that underscore the real-time attributes of embedded systems:

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2.6.1 Real-time Clocks

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Real-time clocks (RTCs) hold a central position in embedded systems, offering an accurate time benchmark that directs the system’s activities. These clocks frequently come with battery backups to maintain reliable timekeeping, even when the primary power source is compromised. The role of RTCs is more critical and widespread in embedded systems compared to general-purpose computing, where timekeeping, while important, usually doesn’t govern the core operations of the system.

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For example, in the realm of industrial automation, RTCs facilitate the precise coordination of tasks, ensuring synchronized and timely processes. They are particularly crucial in scenarios requiring time-stamped data, such as environmental monitoring systems where the accuracy and time relevance of data are imperative.

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2.6.2 Timing and Synchronization

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Timing and synchronization stand as defining features of embedded systems, requiring various components and processes to operate in concert. The essence of a real-time embedded system is shaped by its capability to execute tasks within a specified time window. Such systems often have rigorous timing constraints, necessitating synchronization methods that are both sturdy and exact.

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In the context of automotive control systems, the synchronized and timely operation of diverse sensors and actuators is imperative for both safety and peak performance. This sharply contrasts with general-purpose systems, where timing, though managed, usually lacks immediate and critical consequences.

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2.6.3 Task Management and Scheduling

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In the world of embedded systems, the management and scheduling of tasks are crucial for effective real-time responses. Task schedulers in these systems often use techniques like priority scheduling, where tasks are ranked by importance, allowing higher-priority tasks to interrupt those of lower priority. This is especially critical in systems where some functions have greater urgency.

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For example, in medical devices such as pacemakers, the punctual delivery of electrical impulses is a high-priority task, and the scheduler must give it precedence over all other activities to ensure patient safety. This level of refined scheduling and task management sets embedded systems apart from the more adaptable but less deterministic scheduling seen in general-purpose systems.

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2.6.4 Error Handling and Fault Tolerance

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To enhance their real-time features, embedded systems frequently incorporate mechanisms for error detection and fault resilience. These are engineered to swiftly identify and rectify errors or to sustain system functionality even when faults occur. In aviation control systems, for instance, real-time fault tolerance is essential for maintaining the stability and safety of drones. This meticulous approach to error management is somewhat unique to embedded systems, accentuating the critical nature of many such applications.

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The real-time attributes of embedded systems distinguish them, creating an environment where accuracy, synchrony, and prompt responses are not optional but obligatory. These attributes resonate across a wide range of applications, from automotive systems to industrial automation and healthcare devices, highlighting the role of embedded systems as quiet yet potent conductors of a technologically synchronized world. Through their real-time features, embedded systems offer solutions that not only satisfy functional needs but do so with a degree of precision and dependability that is both extraordinary and essential in today’s world.

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2.7 Security and Reliability

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In an increasingly interconnected and tech-dependent world, the issues of security and reliability have risen to become primary considerations in system engineering. This is especially true for embedded systems, which often serve as key components in critical infrastructures and applications, thereby raising the stakes considerably. Let’s explore the crucial elements that fortify the bastion of security and reliability in embedded systems:

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2.7.1 Secure Boot and Root of Trust

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Embedded systems are increasingly central to a variety of critical applications, making it imperative to assure their authenticity and integrity from the moment they boot up. The secure boot sequence serves as a foundational element in this security framework, permitting the system to run only code that has been authenticated and deemed trustworthy. This is often augmented by a “Root of Trust,” a stable and secure environment, typically hardware-based, that validates the initial firmware and each subsequent layer of software during the boot-up sequence.

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For example, in financial settings involving Point-of-Sale (POS) terminals, a secure boot mechanism guarantees that the firmware remains intact and secure, thereby preventing any malicious alterations that could lead to significant data breaches. Likewise, in the realm of home automation, a strong secure boot process acts as a barrier to unauthorized access, thereby protecting user data and privacy.

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2.7.2 Fault Tolerance

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Fault tolerance is an essential quality in embedded systems, granting them the ability to maintain functionality even when faced with faults or system failures. This resilience is achieved through various means such as redundancy, where vital components are replicated to assume control in the event of a failure, or via sophisticated error detection and correction methods.

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In sectors like aerospace and aviation, fault tolerance is not merely an advantageous feature but an obligatory specification. For instance, aircraft control systems utilize multiple redundant configurations that operate in parallel to assure uninterrupted functionality, even if a component fails. This degree of fault tolerance provides a heightened level of reliability, enabling the system to endure failures without disastrous outcomes, a feature that distinguishes it from conventional computing systems.

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2.7.3 Safety-Critical Systems

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Safety-critical systems are defined as those where a malfunction could lead to loss of life, substantial property damage, or environmental degradation. Such systems demand rigorous design protocols to guarantee the highest levels of reliability and safety. Embedded systems falling under this classification often comply with stringent development guidelines and are subject to exhaustive testing to confirm their safety and reliability metrics.

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For instance, in automotive safety features like Anti-lock Braking Systems (ABS) and Electronic Stability Control (ESC), embedded controllers are crucial. These controllers are engineered in accordance with rigorous standards like ISO 26262, ensuring they meet the elevated safety and reliability criteria essential for safeguarding lives. In the healthcare sector, devices such as pacemakers and infusion pumps are categorized as safety-critical, where the dependability of embedded systems can quite literally be life-altering.

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The focus on security and reliability in embedded systems is of paramount importance, a point that is often underestimated by many. As these systems become increasingly woven into the fabric of our everyday lives and critical infrastructure, the principles of security and reliability serve as guiding lights in their development and deployment. Through features like secure booting and fault tolerance, these systems offer not just operational efficiency but also a layer of trust and security, providing a steadfast and secure anchor in a rapidly evolving technological landscape. These foundational tenets shape today’s embedded systems, molding them into dependable stewards and proficient operators in various critical domains of contemporary society.

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2.8 Future Trends and Challenges

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Arm, the leading producer of microcontrollers, has reached a milestone by shipping an unprecedented 8.0 billion chips, either directly or through its partners. This takes the total number of chips shipped to date to an astounding quarter of a trillion, or 250 billion (ARM.com)!

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+ARM.com. “The Future Is Being Built on Arm: Market Diversification Continues to Drive Strong Royalty and Licensing Growth as Ecosystem Reaches Quarter of a Trillion Chips Milestone – Arm®.” https://www.arm.com/company/news/2023/02/arm-announces-q3-fy22-results. +

As we find ourselves at the threshold of a new era marked by extraordinary growth in the embedded systems sector, it becomes both exhilarating and imperative to scrutinize the emerging trends and challenges that lie ahead. From the expanding horizons of edge computing to the imperatives of scalability, the landscape is poised for transformation, unveiling new realms of both opportunities and challenges. Let’s explore the evolving frontier that awaits embedded systems:

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2.8.1 Edge Computing and IoT

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With the rapid expansion of the Internet of Things (IoT), edge computing is gaining increasing prominence. Essentially, edge computing enables data to be processed closer to its source, thereby reducing latency and alleviating the burden on centralized data centers. This shift in computing paradigms is anticipated to revolutionize embedded systems, endowing them with enhanced processing power and the intelligence to perform intricate tasks on-site.

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Additionally, as the IoT is projected to include billions of interconnected devices worldwide, embedded systems are slated to be the linchpin in ensuring smooth connectivity and interoperability among a diverse set of devices. This interconnected ecosystem is expected to enable real-time analytics and decision-making, laying the groundwork for more intelligent cities, industries, and households. The challenge resides in crafting systems that are secure, energy-efficient, and adept at managing the anticipated data deluge effectively.

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2.8.2 Scalability and Upgradation

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As the landscape of embedded systems continues its evolutionary trajectory, the focus will increasingly turn towards scalability and ease of upgradation. Systems will be required to adapt to evolving technologies and user needs without undergoing extensive modifications. This necessitates modular architectures and adherence to open standards, facilitating the effortless incorporation of new functionalities and features.

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Moreover, in light of rapid technological advancements, embedded systems will need to incorporate capabilities for remote updates and maintenance to ensure their continued relevance and longevity. The responsibility will fall on the shoulders of developers and manufacturers to engineer systems that not only satisfy current needs but are also prepared for future enhancements, thereby securing a path for sustainable and progressive development.

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2.8.3 Market Opportunities

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The market landscape for embedded systems is on the cusp of dynamic changes. As various industries accelerate their adoption of automation and digital transformation, the demand for advanced embedded systems is set to skyrocket. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into embedded systems is expected to offer unparalleled levels of intelligence and automation.

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At the same time, burgeoning opportunities are emerging in sectors like consumer electronics, automotive, healthcare, and industrial applications. While this growth presents enormous potential for innovation, it also introduces challenges such as heightened competition and the necessity for adherence to evolving regulatory frameworks. Companies entering this arena will need to exhibit agility, innovation, and adaptability to the shifting market conditions in order to establish a competitive edge.

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2.9 Conclusion

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The table provides a side-by-side comparison between these two distinct types of computing systems, covering a range of categories including processing power, memory capabilities, user interface, and real-time functionalities, among others. The aim of this comparative analysis is to offer readers a concise yet thorough understanding of the unique attributes and specificities of both conventional and embedded computing systems. This, in turn, enables a more nuanced and informed grasp of their respective roles in today’s computing landscape.

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CategoryTraditional Computing SystemEmbedded System Architecture
Hardware Characteristics
Processing PowerHigh (Multi-core processors)Moderate to Low (Single/Multi-core, optimized for specific tasks)
MemoryHigh (Upgradable)Limited (Fixed)
StorageHigh (Upgradable)Limited (Fixed or expandable to a certain extent)
Hardware ScalabilityHigh (Can upgrade various components)Low (Hardware is often fixed and focused)
Software Characteristics
Operating SystemGeneral Purpose (Windows, Linux, macOS)Real-Time Operating System (RTOS) or No OS
Development FlexibilityHigh (Supports multiple programming languages and frameworks)Moderate (Focused on specific programming languages and tools)
Performance & Efficiency
Power ConsumptionHighLow (Optimized for energy efficiency)
Real-Time CapabilitiesLimited (Not optimized for real-time tasks)High (Designed for real-time tasks)
User Interaction
User InterfaceComplex (GUI-Based)Simple or None (Can be GUI, command-line, or none)
ConnectivityExtensive (Multiple ports and connectivity options)Limited (Focused on necessary connectivity options)
Lifecycle & Maintenance
MaintenanceRegular Maintenance RequiredLow Maintenance (Set up to run specific tasks consistently)
LifecycleShorter (Due to rapid technological advancements)Longer (Designed to perform specific tasks over a long period)
Cost and Use Cases
CostVariable (Can be high depending on specifications)Generally Lower (Due to focused functionalities)
Use CasesGeneral (Various applications across sectors)Specific (Dedicated to particular tasks or applications)
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As we gaze into the future, it’s clear that the realm of embedded systems stands on the cusp of a transformative era, characterized by groundbreaking innovations, abundant opportunities, and formidable challenges. The horizon is replete with the promise of enhanced connectivity, heightened intelligence, and superior efficiency, carving out a trajectory where embedded systems will serve as the guiding force behind society’s technological progress. The path forward is one of discovery and adaptability, where the confluence of technological prowess and creative ingenuity will sculpt a future that is not only rich in technological advancements but also attuned to the intricate and continually shifting needs of a dynamic global landscape. It’s a field teeming with possibilities, inviting trailblazers to embark on a journey to define the parameters of a bright and flourishing future.

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2.10 Exercises

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Now would be a great time for you to get your hands on a real embedded device, and get it setup.

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If you want to play with an embedded system, try out the Nicla Vision

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Setup Nicla Vision

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MACHINE LEARNING SYSTEMS

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for TinyML

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Abstract
+ Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems. +
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Preface

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Welcome to “Machine Learning Systems for TinyML” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.

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Our aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.

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“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb

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This isn’t just a static textbook; it’s a living, breathing document. We’re making it open-source and continually updated to meet the ever-changing needs of this dynamic field. Expect a rich blend of expert knowledge that guides you through the complex interplay between cutting-edge algorithms and the foundational principles that make them work. We’re setting the stage for the next big leap in tech innovation.

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Why We Wrote This Book

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We’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Machine Learning Systems for TinyML.” We’re going beyond the traditional textbook model to create a living knowledge hub.

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The book covers principles, algorithms, and real-world application case studies, aiming to give you a deep understanding that will help you navigate the ever-changing landscape of embedded AI. By keeping it open, we’re not just making learning accessible; we’re inviting new ideas and ongoing improvements. In short, we’re building a community where knowledge is free to grow and light the way forward in global embedded AI tech.

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What You’ll Need to Know

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Don’t worry, you don’t need to be a machine learning whiz to dive into this book. All you really need is a basic understanding of systems and a curiosity to explore how embedded hardware, AI, and software come together. This is where innovation happens, and a basic grasp of how systems work will be your compass.

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We’re also focusing on the exciting overlaps between these fields, aiming to create a learning environment where traditional boundaries fade away, making room for a more holistic, integrated view of modern tech. Your interest in embedded AI and low-level software will guide you through a rich and rewarding learning experience.

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Book Conventions

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For details on the conventions used in this book, check out the Conventions section.

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Want to Help Out?

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If you’re interested in contributing, you can find the guidelines here.

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Get in Touch

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Got questions or feedback? Feel free to e-mail us.

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Contributors

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A big thanks to everyone who’s helped make this book what it is! You can see the full list of contributors here.

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1  Introduction

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1.1 Overview

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Welcome to this comprehensive exploration of Tiny Machine Learning (TinyML). This book aims to bridge the gap between intricate machine learning theories and their practical applications on small devices. Whether you’re a newcomer, an industry professional, or an academic researcher, this book offers a balanced mix of essential theory and hands-on insights into TinyML.

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1.2 What’s Inside

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The book starts with a foundational look at embedded systems and machine learning, focusing on deep learning methods due to their effectiveness across various tasks. We then guide you through the entire machine learning workflow, from data engineering to advanced model training.

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We also delve into TinyML model optimization and deployment, with a special emphasis on on-device learning. You’ll find comprehensive discussions on current hardware acceleration techniques and model lifecycle management. Additionally, we explore the sustainability and ecological impact of AI, and how TinyML fits into this larger conversation.

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The book concludes with a look at the exciting possibilities of generative AI within the TinyML context.

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1.3 Chapter Breakdown

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Here’s a closer look at what each chapter covers:

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Chapter 1: Introduction This chapter sets the stage, providing an overview of embedded AI and laying the groundwork for the chapters that follow.

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Chapter 2: Embedded Systems We introduce the basics of embedded systems, the platforms where AI algorithms are widely applied.

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Chapter 3: Deep Learning Primer This chapter offers a comprehensive introduction to the algorithms and principles that underpin AI applications in embedded systems.

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Chapter 4: Embedded AI Here, we explore how machine learning techniques can be integrated into embedded systems, enabling intelligent functionalities.

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Chapter 5: AI Workflow This chapter breaks down the machine learning workflow, offering insights into the steps leading to proficient AI applications.

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Chapter 6: Data Engineering We focus on the importance of data in AI systems, discussing how to effectively manage and organize data.

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Chapter 7: AI Training This chapter delves into model training, exploring techniques for developing efficient and reliable models.

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Chapter 8: On-Device AI Here, we discuss strategies for achieving efficiency in AI applications, from computational resource optimization to performance enhancement.

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Chapter 9: Model Optimizations We explore various avenues for optimizing AI models for seamless integration into embedded systems.

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Chapter 10: AI Frameworks This chapter reviews different frameworks for developing machine learning models, guiding you in choosing the most suitable one for your projects.

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Chapter 11: AI Acceleration We discuss the role of specialized hardware in enhancing the performance of embedded AI systems.

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Chapter 12: Benchmarking AI This chapter focuses on how to evaluate AI systems through systematic benchmarking methods.

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Chapter 13: On-Device Learning We explore techniques for localized learning, which enhances both efficiency and privacy.

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Chapter 14: Embedded AIOps This chapter looks at the processes involved in the seamless integration, monitoring, and maintenance of AI functionalities in embedded systems.

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Chapter 15: Privacy and Security As AI becomes more ubiquitous, this chapter addresses the crucial aspects of privacy and security in embedded AI systems.

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Chapter 16: Responsible AI We discuss the ethical principles guiding the responsible use of AI, focusing on fairness, accountability, and transparency.

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Chapter 17: AI Sustainability This chapter explores practices and strategies for sustainable AI, ensuring long-term viability and reduced environmental impact.

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Chapter 18: Generative AI We explore the algorithms and techniques behind generative AI, opening avenues for innovation and creativity.

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Chapter 19: AI for Good

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We highlight positive applications of TinyML in areas like healthcare, agriculture, and conservation.

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1.4 How to Navigate This Book

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To get the most out of this book, consider the following structured approach:

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  1. Foundational Knowledge (Chapters 1-4): Start by building a strong foundation with the initial chapters, which provide the context and groundwork for more advanced topics.

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  2. Practical Insights (Chapters 5-14): With a solid foundation, move on to the chapters that offer practical insights into machine learning workflows, data engineering, and optimizations. Engage in hands-on exercises and case studies to solidify your understanding.

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  3. Ethics and Sustainability (Chapters 15-17): These chapters offer a critical perspective on the ethical and sustainable practices in AI, encouraging responsible AI deployment.

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  4. Future Trends (Chapter 18): Conclude your journey by exploring the exciting domain of generative AI, which offers a glimpse into the future of the field.

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  5. Interconnected Learning: While the chapters are designed for a progressive learning curve, feel free to navigate non-linearly based on your interests and needs.

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  6. Practical Applications: Throughout the book, try to relate theoretical knowledge to real-world applications. Engage with practical exercises and case studies to bridge the gap between theory and practice.

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  7. Discussion and Networking: Engage in discussions, forums, or study groups to share insights and debate concepts, which can deepen your understanding.

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  8. Revisit and Reflect: Given the dynamic nature of AI, don’t hesitate to revisit chapters. A second reading can offer new insights and foster continuous learning.

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By adopting this structured yet flexible approach, you’re setting the stage for a fulfilling and enriching learning experience.

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1.5 The Road Ahead

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As we navigate the multifaceted world of embedded AI, we’ll cover a broad range of topics, from computational theories and engineering principles to ethical considerations and innovative applications. Each chapter unveils a piece of this expansive puzzle, inviting you to forge new connections, ignite discussions, and fuel a perpetual curiosity about embedded AI. Join us as we explore this fascinating field, which is not only reshaping embedded systems but also redrawing the contours of our technological future.

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1.6 Contribute Back

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Learning in the fast-paced world of embedded AI is a collaborative journey. This book aims to nurture a vibrant community of learners, innovators, and contributors. As you explore the concepts and engage with the exercises, we encourage you to share your insights and experiences. Whether it’s a novel approach, an interesting application, or a thought-provoking question, your contributions can enrich the learning ecosystem. Engage in discussions, offer and seek guidance, and collaborate on projects to foster a culture of mutual growth and learning. By sharing knowledge, you play a pivotal role in fostering a globally connected, informed, and empowered community.

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+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/optimizations.qmd b/optimizations.qmd index b6bf429b..6e3b2b50 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -117,7 +117,7 @@ The following compact table provides a concise comparison between structured and | **Implementation Complexity**| Often simpler to implement and manage due to maintaining network structure. | Can be complex to manage and compute due to sparse representations. | | **Fine-Tuning Complexity** | May require less complex fine-tuning strategies post-pruning. | Might necessitate more complex retraining or fine-tuning strategies post-pruning. | -![A visualization showing the differences and examples between unstructured and structured pruning. Observe that unstructured pruning can lead to models that no longer obey high-level structural guaruntees of their original unpruned counterparts: the left network is no longer a fully connected network after pruning. Structured pruning on the other hand maintains those invariants: in the middle, the fully connected network is pruned in a way that the pruned network is still fully connected; likewise, the CNN maintains its convolutional structure, albeit with fewer filters (Credit: EURASIP).](images/modeloptimization_pruning_comparison.png) +![A visualization showing the differences and examples between unstructured and structured pruning. Observe that unstructured pruning can lead to models that no longer obey high-level structural guaruntees of their original unpruned counterparts: the left network is no longer a fully connected network after pruning. Structured pruning on the other hand maintains those invariants: in the middle, the fully connected network is pruned in a way that the pruned network is still fully connected; likewise, the CNN maintains its convolutional structure, albeit with fewer filters (@qi_efficient_2021).](images/modeloptimization_pruning_comparison.png) #### Lottery Ticket Hypothesis @@ -127,7 +127,7 @@ A breakthrough finding that catalyzed this evolution was the [lottery ticket hyp More formally, the lottery ticket hypothesis is a concept in deep learning that suggests that within a neural network, there exist sparse subnetworks (or "winning tickets") that, when initialized with the right weights, are capable of achieving high training convergence and inference performance on a given task. The intuition behind this hypothesis is that, during the training process of a neural network, many neurons and connections become redundant or unimportant, particularly with the inclusion of training techniques encouraging redundancy like dropout. Identifying, pruning out, and initializing these "winning tickets'' allows for faster training and more efficient models, as they contain the essential model decision information for the task. Furthermore, as generally known with the bias-variance tradeoff theory, these tickets suffer less from overparameterization and thus generalize better rather than overfitting to the task. ![](RackMultipart20231019-1-ips613_html_8215915cde9ac59.png) -![An example experiment from the lottery ticket hypothesis showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios: note the first plot showing how pruning is able to reveal a subnetwork nearly one-fifth the size that trains to a higher test accuracy faster than the unpruned network. However, further note how in the second plot that further pruned models in models that both train slower and are not able to achieve that same maximal test accuracy due to the lower number of parameters (Credit: ICLR).](images/modeloptimization_lottery_ticket_hypothesis.png) +![An example experiment from the lottery ticket hypothesis showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios: note the first plot showing how pruning is able to reveal a subnetwork nearly one-fifth the size that trains to a higher test accuracy faster than the unpruned network. However, further note how in the second plot that further pruned models in models that both train slower and are not able to achieve that same maximal test accuracy due to the lower number of parameters (@frankle_lottery_2019).](images/modeloptimization_lottery_ticket_hypothesis.png) #### Challenges & Limitations @@ -179,7 +179,7 @@ Another core concept is "temperature scaling" in the softmax function. It plays These components, when adeptly configured and harmonized, enable the student model to assimilate the teacher model's knowledge, crafting a pathway towards efficient and robust smaller models that retain the predictive prowess of their larger counterparts. -![A visualization of the training procedure of knowledge distillation. Note how the logits or soft labels of the teacher model are used to provide a distillation loss for the student model to learn from (Credit: IntelLabs).](images/modeloptimization_knowledge_distillation.png) +![A visualization of the training procedure of knowledge distillation. Note how the logits or soft labels of the teacher model are used to provide a distillation loss for the student model to learn from (@noauthor_knowledge_nodate).](images/modeloptimization_knowledge_distillation.png) ##### Challenges @@ -199,7 +199,8 @@ One of the seminal works in the realm of matrix factorization, particularly in t The main advantage of low-rank matrix factorization lies in its ability to reduce data dimensionality as shown in the image below where there are fewer parameters to store, making it computationally more efficient and reducing storage requirements at the cost of some additional compute. This can lead to faster computations and more compact data representations, which is especially valuable when dealing with large datasets. Additionally, it may aid in noise reduction and can reveal underlying patterns and relationships in the data. -![A visualization showing the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix $M$ can be approximated by the product of matrices $L_k$ and $R_k^T$. For intuition, most fully connected layers in networks are stored as a projection matrix $M$, which requires $m \times n$ parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store $m \times k + k\times n$ parameters in terms of storage while incurring an additional compute cost of the matrix multiplication.__So long as $k \< n/2$, this factorization has fewer parameters total to store while adding a computation of runtime $O(mkn)$ (Credit: Medium).](images/modeloptimization_low_rank_matrix_factorization.png) +![A visualization showing the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix $M$ can be approximated by the product of matrices $L_k$ and $R_k^T$. For intuition, most fully connected layers in networks are stored as a projection matrix $M$, which requires $m \times n$ parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store $m \times k + k\times n$ parameters in terms of storage while incurring an additional compute cost of the matrix multiplication. So long as $k \< n/2$, this factorization has fewer parameters total to store while adding a computation of runtime $O(mkn)$ (@noauthor_deep_nodate).](images/modeloptimization_low_rank_matrix_factorization.png) + ##### Challenges But practitioners and researchers encounter a spectrum of challenges and considerations that necessitate careful attention and strategic approaches. As with any lossy compression technique, we may lose information during this approximation process: choosing the correct rank that balances the information lost and the computational costs is tricky as well and adds an additional hyper-parameter to tune for. @@ -218,7 +219,7 @@ Similar to low-rank matrix factorization, more complex models may store weights The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Applications"](https://epubs.siam.org/doi/abs/10.1137/07070111X) (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments . -![A visualization showing the decrease in parameterization enabled by tensor decomposition. Observe how the shown three-dimensional tensor $y$ can be approximated by three matrices, all of lower rank, greatly reducing the number of parameters to be held in memory. (Credit: Medium).](images/modeloptimization_tensor_decomposition.png) +![A visualization showing the decrease in parameterization enabled by tensor decomposition. Observe how the shown three-dimensional tensor $y$ can be approximated by three matrices, all of lower rank, greatly reducing the number of parameters to be held in memory (@chen__inpainting_2022).](images/modeloptimization_tensor_decomposition.png) ### Edge-Aware Model Design @@ -230,7 +231,7 @@ As covered in previous sections, edge devices are constrained specifically with One edge friendly architecture design is depthwise separable convolutions. Commonly used in deep learning for image processing, it consists of two distinct steps: the first is the depthwise convolution, where each input channel is convolved independently with its own set of learnable filters. This step reduces computational complexity by a significant margin compared to standard convolutions, as it drastically reduces the number of parameters and computations involved. The second step is the pointwise convolution, which combines the output of the depthwise convolution channels through a 1x1 convolution, creating inter-channel interactions. This approach offers several advantages. Pros include reduced model size, faster inference times, and often better generalization due to fewer parameters, making it suitable for mobile and embedded applications. However, depthwise separable convolutions may not capture complex spatial interactions as effectively as standard convolutions and might require more depth (layers) to achieve the same level of representational power, potentially leading to longer training times. Nonetheless, their efficiency in terms of parameters and computation makes them a popular choice in modern convolutional neural network architectures. -![A visualization showing each of the individual operations behind a single depthwise separable convolution: first, we give the input image a convolution without modifying the depth. Once those convolutions are completed, we then do a pointwise 1-by-1 convolution to get to the desired number of channels. This reduces the number of parameters, making it a key TinyML technique (Credit: AnalyticsVidhya).](images/modeloptimization_depthwise_separable_convolution.png) +![A visualization showing each of the individual operations behind a single depthwise separable convolution: first, we give the input image a convolution without modifying the depth. Once those convolutions are completed, we then do a pointwise 1-by-1 convolution to get to the desired number of channels. This reduces the number of parameters, making it a key TinyML technique (@noauthor_introduction_nodate).](images/modeloptimization_depthwise_separable_convolution.png) #### Example Model Architectures diff --git a/references.bib b/references.bib index 0fdbfede..360213c8 100644 --- a/references.bib +++ b/references.bib @@ -393,13 +393,17 @@ @article{qi_efficient_2021 @misc{noauthor_knowledge_nodate, title = {Knowledge {Distillation} - {Neural} {Network} {Distiller}}, url = {https://intellabs.github.io/distiller/knowledge_distillation.html}, + author = {IntelLabs}, urldate = {2023-10-20}, + year = {2023} } @misc{noauthor_deep_nodate, title = {Deep {Learning} {Model} {Compression} (ii) {\textbar} by {Ivy} {Gu} {\textbar} {Medium}}, url = {https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453}, urldate = {2023-10-20}, + author = {Ivy Gu}, + year = {2023} } @misc{lu_notes_2016, @@ -479,6 +483,8 @@ @misc{noauthor_introduction_nodate title = {An {Introduction} to {Separable} {Convolutions} - {Analytics} {Vidhya}}, url = {https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/}, urldate = {2023-10-20}, + author = {Hegde, Sumant}, + year = {2023} } @misc{iandola_squeezenet_2016, diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 00000000..94f19404 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2018 @@ +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + 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document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

    ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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new file mode 100644 index 00000000..3b21201f --- /dev/null +++ b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 00000000..619c57cc --- /dev/null +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof 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a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 00000000..f5d852d1 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} From d339dc6b0babafca1a4583f51b75f8148091ea9b Mon Sep 17 00:00:00 2001 From: Jeffrey Ma Date: Mon, 30 Oct 2023 15:58:37 -0400 Subject: [PATCH 02/12] fix --- about.html | 735 ------ acknowledgements.html | 673 ------ contributors.html | 714 ------ copyright.html | 672 ------ dedication.html | 668 ------ embedded_sys.html | 1231 ---------- front.html | 663 ------ index.html | 722 ------ introduction.html | 735 ------ optimizations.qmd | 22 +- site_libs/bootstrap/bootstrap-icons.css | 2018 ----------------- site_libs/bootstrap/bootstrap-icons.woff | Bin 164168 -> 0 bytes site_libs/bootstrap/bootstrap.min.css | 10 - site_libs/bootstrap/bootstrap.min.js | 7 - site_libs/clipboard/clipboard.min.js | 7 - site_libs/quarto-html/anchor.min.js | 9 - site_libs/quarto-html/popper.min.js | 6 - .../quarto-syntax-highlighting.css | 203 -- site_libs/quarto-html/quarto.js | 902 -------- site_libs/quarto-html/tippy.css | 1 - site_libs/quarto-html/tippy.umd.min.js | 2 - site_libs/quarto-nav/quarto-nav.js | 277 --- site_libs/quarto-search/autocomplete.umd.js | 3 - site_libs/quarto-search/fuse.min.js | 9 - site_libs/quarto-search/quarto-search.js | 1140 ---------- 25 files changed, 11 insertions(+), 11418 deletions(-) delete mode 100644 about.html delete mode 100644 acknowledgements.html delete mode 100644 contributors.html delete mode 100644 copyright.html delete mode 100644 dedication.html delete mode 100644 embedded_sys.html delete mode 100644 front.html delete mode 100644 index.html delete mode 100644 introduction.html delete mode 100644 site_libs/bootstrap/bootstrap-icons.css delete mode 100644 site_libs/bootstrap/bootstrap-icons.woff delete mode 100644 site_libs/bootstrap/bootstrap.min.css delete mode 100644 site_libs/bootstrap/bootstrap.min.js delete mode 100644 site_libs/clipboard/clipboard.min.js delete mode 100644 site_libs/quarto-html/anchor.min.js delete mode 100644 site_libs/quarto-html/popper.min.js delete mode 100644 site_libs/quarto-html/quarto-syntax-highlighting.css delete mode 100644 site_libs/quarto-html/quarto.js delete mode 100644 site_libs/quarto-html/tippy.css delete mode 100644 site_libs/quarto-html/tippy.umd.min.js delete mode 100644 site_libs/quarto-nav/quarto-nav.js delete mode 100644 site_libs/quarto-search/autocomplete.umd.js delete mode 100644 site_libs/quarto-search/fuse.min.js delete mode 100644 site_libs/quarto-search/quarto-search.js diff --git a/about.html b/about.html deleted file mode 100644 index b9fc4508..00000000 --- a/about.html +++ /dev/null @@ -1,735 +0,0 @@ - - - - - - - - - - -MACHINE LEARNING SYSTEMS - About the Book - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -     - -
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    About the Book

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    Overview

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    Welcome to this collaborative project initiated by the CS249r Tiny Machine Learning class at Harvard University. Our goal is to make this book a community resource that assists educators and learners in understanding TinyML. The book will be regularly updated to reflect new insights into TinyML and effective teaching methods.

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    Topics Explored

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    This book offers a comprehensive look at various aspects of embedded machine learning. The topics we delve into include:

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    • Introduction and Overview of Embedded Machine Learning
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    • Data Engineering Techniques
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    • Efficient Representation and Compression of Models
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    • Performance Metrics and Benchmarking for Machine Learning Systems
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    • Edge Learning
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    • Hardware Acceleration Options: GPUs, TPUs, and FPGAs
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    • Operational Aspects of Embedded Machine Learning
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    • Security and Privacy in On-Device Machine Learning
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    • Ethical Considerations in AI
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    • Generative AI in Edge Computing
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    By the time you finish this book, you’ll have a foundational understanding of machine learning and the Internet of Things. You’ll also learn about real-world applications of embedded machine learning systems and gain practical experience through project-based assignments.

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    Who Should Read This

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    This book is tailored for those new to the exciting field of tiny machine learning (TinyML). It starts with the basics of machine learning and embedded systems and progresses to more advanced topics relevant to the TinyML community and broader research areas. The book is particularly beneficial for:

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    • Embedded Systems Engineers: For engineers in the embedded systems domain, this book serves as an excellent guide to TinyML, helping them create intelligent applications on resource-limited platforms.

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    • Students in Computer Science and Electrical Engineering: This book is a useful resource for students studying computer science and electrical engineering. It introduces them to the methods, algorithms, and techniques used in TinyML, preparing them for real-world challenges in embedded machine learning.

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    • Researchers and Academics: Those involved in machine learning, computer vision, and signal processing research will find this book insightful. It sheds light on the unique challenges of running machine learning algorithms on low-power, low-memory devices.

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    • Industry Professionals: If you’re working in areas like IoT, robotics, wearable tech, or smart devices, this book will equip you with the knowledge you need to add machine learning features to your products.

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    Key Learning Outcomes

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    Readers will acquire skills in training and deploying deep neural network models on resource-limited microcontrollers, along with understanding the broader challenges involved in their design, development, and deployment. Specifically, you’ll learn about:

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    • Foundational Concepts in Machine Learning
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    • Fundamentals of Embedded AI
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    • Techniques for Training Models for Embedded Systems
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    • Real-world Applications of Embedded AI
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    • Current Challenges and Future Trends in Embedded AI
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    Our aim is to make this book a comprehensive resource for anyone interested in developing intelligent applications on embedded systems. Upon completing the book, you’ll be well-equipped to design and implement your own machine learning-enabled projects.

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    Prerequisites for Readers

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    • Basic Programming Skills: We recommend that you have some prior programming experience, ideally in Python. A grasp of variables, data types, and control structures will make it easier to engage with the book.

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    • Some Machine Learning Knowledge: While not mandatory, a basic understanding of machine learning concepts will help you absorb the material more readily. If you’re new to the field, the book provides enough background information to get you up to speed.

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    • Python Programming (Optional): If you’re familiar with Python, you’ll find it easier to engage with the coding sections of the book. Knowing libraries like NumPy, scikit-learn, and TensorFlow will be particularly helpful.

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    • Willingness to Learn: The book is designed to be accessible to a broad audience, with varying levels of technical expertise. A willingness to challenge yourself and engage in practical exercises will help you get the most out of it.

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    • Resource Availability: For the hands-on aspects, you’ll need a computer with Python and the relevant libraries installed. Optional access to an embedded development board or microcontroller will also be beneficial for experimenting with machine learning model deployment.

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    By meeting these prerequisites, you’ll be well-positioned to deepen your understanding of TinyML, engage in coding exercises, and even implement practical applications on embedded devices.

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    Edited by Prof. Vijay Janapa Reddi (Harvard University)
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    This book was built with Quarto.
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    Acknowledgements

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    Assembling this book has been an incredible journey, spanning several years of hard work. The initial idea for this book sprang from the tinyML edX course, and its realization would not have been possible without the invaluable contributions of countless individuals. We are deeply indebted to the researchers whose groundbreaking work laid the foundation for this book.

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    We extend our heartfelt gratitude to the GitHub community. Whether you contributed an entire section, a single sentence, or merely corrected a typo, your efforts have significantly enhanced this book. We deeply appreciate everyone’s time, expertise, and commitment. This book is as much yours as it is ours.

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    Special thanks go to Professor Vijay Janapa Reddi, whose belief in the transformative power of open-source communities and invaluable guidance have been our guiding light from the outset.

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    We also owe a great deal to the team at GitHub. You’ve revolutionized the way people collaborate, and this book stands as a testament to what can be achieved when barriers to global cooperation are removed.

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    To all who pick up this book—thank you! We wrote it with you in mind, hoping to provoke thought, inspire questions, and perhaps even ignite a spark of inspiration. After all, what is the point of writing if no one is reading?

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    Last but certainly not least, our deepest thanks go to our friends, families, mentors, and all the kind souls who have supported us emotionally and intellectually as this book came to fruition.

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    Contributors

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    We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, and time to enhance both the content and codebase of this project. Below you will find a list of all contributors. If you would like to contribute to this project, please see our GitHub page.

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    -Vijay Janapa Reddi
    Vijay Janapa Reddi
    📖 -    		 -ishapira
    ishapira
    📖 -    		 -Ikechukwu Uchendu
    Ikechukwu Uchendu
    📖 -    		 -Shvetank Prakash
    Shvetank Prakash
    📖 -    		 -oishib
    oishib
    📖 -    		 -sjohri20
    sjohri20
    📖 -    		 -Marco Zennaro
    Marco Zennaro
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    -Jessica Quaye
    Jessica Quaye
    📖 -    		 -Marcelo Rovai
    Marcelo Rovai
    📖 -    		 -Matthew Stewart
    Matthew Stewart
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    Copyright

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    This book is open-source and developed collaboratively through GitHub. Unless otherwise stated, this work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). You can find the full text of the license here.

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    Contributors to this project have dedicated their contributions to the public domain or under the same open license as the original project. While the contributions are collaborative, each contributor retains copyright in their respective contributions.

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    For details on authorship, contributions, and how to contribute, please see the project repository on GitHub.

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    All trademarks and registered trademarks mentioned in this book are the property of their respective owners.

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    The information provided in this book is believed to be accurate and reliable. However, the authors, editors, and publishers cannot be held liable for any damages caused or alleged to be caused either directly or indirectly by the information contained in this book.

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    Dedication

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    This book is a testament to the idea that, in the vast expanse of technology and innovation, it’s not always the largest systems, but the smallest ones, that can change the world.

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    2  Embedded Systems

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    In the domain of TinyML, embedded systems serve as the bedrock, providing a robust platform where intelligent algorithms can function both efficiently and effectively. Defined by their specialized roles and real-time computational capabilities, these systems act as the convergence point where data and computation intersect on a micro-scale. Tailored to meet the demands of specific tasks, they excel in optimizing performance, energy usage, and spatial efficiency—key considerations in the successful implementation of TinyML solutions.

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    As we journey further into this chapter, we will demystify the intricate yet captivating realm of embedded systems, gaining insights into their structural design, operational features, and the crucial part they play in enabling TinyML applications. From an introduction to the fundamentals of microcontroller units to a deep dive into the interfaces and peripherals that amplify their capabilities, this chapter aims to be a comprehensive guide for understanding the nuanced aspects of embedded systems within the TinyML landscape.

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    • Understand the definition, characteristics, history, and importance of embedded systems, especially in relation to tinyML.

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    • Examine the embedded system architecture including microcontrollers vs microprocessors, memory types and management, and System on Chip (SoC).

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    • Explore embedded system programming including languages like C and Python, firmware development, and real-time operating systems (RTOS).

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    • Discuss interfaces and peripherals like digital/analog I/O, communication protocols, etc.

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    • Analyze power management considerations, energy-efficient design, and battery management.

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    • Understand real-time characteristics of embedded systems including clocks, timing, task scheduling, and error handling.

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    • Evaluate security, reliability and safety-critical aspects of embedded systems.

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    • Identify future trends and challenges like edge computing, scalability, and market opportunities.

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    2.1 Basics and Components

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    2.1.1 Definition and Characteristics

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    Embedded systems are specialized forms of computing that do not resemble traditional computers. These systems are dedicated to particular tasks and integrate as components within larger devices. Unlike general-purpose computers capable of running a multitude of applications, embedded systems are designed to execute predefined tasks, often with stringent requirements. Due to their task-specific nature, their architecture is optimized for performance and reliability. The defining traits of these systems include:

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    1. Dedicated Functionality: These systems are engineered to carry out a specific function or a cluster of closely related functions. This specialization allows for optimization, resulting in enhanced performance and reliability.

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    2. Real-Time Operation: A large number of embedded systems function in real-time, necessitating immediate responses to environmental inputs or changes within a set time frame.

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    3. Integration with Physical Hardware: Unlike general-purpose computing systems, embedded systems are tightly integrated with physical components, making them more mechanically oriented.

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    4. Long Lifecycle: Typically, these systems have an extended lifecycle, continuing to operate for many years post their initial deployment.

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    5. Resource Constraints: Often operating under resource limitations, embedded systems require efficient algorithms and software due to restricted computational power and memory.

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    2.1.2 Historical Background

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    The lineage of embedded systems dates back to the 1960s, marked by the introduction of the first microprocessor, labeled as Figure fig-Intel4004. This groundbreaking development led to the creation of the inaugural embedded system used in the Apollo Guidance Computer, the primary navigational system for the Apollo spacecraft. Over subsequent years, the domain has expanded remarkably, finding utility in diverse sectors such as automotive electronics, consumer electronics, telecommunications, and healthcare.

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    Figure 2.1: Intel 4004.
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    2.1.3 Importance in tinyML

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    Within the tinyML framework, embedded systems constitute a vital frontier. The direct integration of machine learning algorithms into these systems enables intelligent, edge-based decision-making, thereby minimizing latency and bolstering security. Here are several factors that underscore the importance of embedded systems in the tinyML ecosystem:

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    1. Edge Computing: By localizing computation near the data source, embedded systems amplify efficiency and diminish the need for continuous interaction with centralized data repositories.

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    2. Low Power Consumption: Designed for minimal energy usage, embedded systems in tinyML are particularly suited for battery-dependent devices and Internet of Things applications.

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    3. Real-Time Analysis and Decision Making: These systems can conduct instantaneous data analysis, facilitating immediate decisions based on the generated insights.

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    4. Security and Privacy: Local data processing on embedded systems enhances security and privacy by reducing the likelihood of data interception during transmission.

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    5. Cost-Effective: The deployment of machine learning models on embedded systems can be economically advantageous, particularly when data transmission and cloud storage could incur substantial costs.

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    As we progress further into this chapter, we will uncover the complexities that dictate the operations of embedded systems and examine how they serve as the foundational layer upon which tinyML is built, heralding a future filled with integrated, intelligent, and efficient devices and systems.

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    2.2 Embedded System Architecture

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    The architectural layout of embedded systems serves as the schematic that outlines the structure and operations of these specialized entities. It sheds light on the interactions and collaborations among various components within an embedded system. This section will dissect the key elements of the architecture, including microcontrollers, microprocessors, diverse types of memory and their management, as well as the complexities of System on Chip (SoC).

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    2.2.1 Microcontrollers vs Microprocessors

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    Comprehending the distinctions between microcontrollers and microprocessors is essential for understanding the basics of embedded system architecture. In this section, we will explore the unique attributes of each:

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    • Microcontrollers

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      Microcontrollers are compact, integrated circuits engineered to control specific functions within an embedded system. They incorporate a processor, memory, and input/output peripherals within a single unit, as depicted in Figure fig-microcontroller, simplifying the overall system design. Microcontrollers are generally employed in applications where computational demands are moderate and cost-effectiveness is a primary consideration.

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    Figure 2.2: Microcontrollers
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    • Microprocessors

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      In contrast, microprocessors are more intricate and serve as the central processing unit within a system. They lack the integrated memory and input/output peripherals commonly found in microcontrollers. These processors are typically present in systems requiring elevated computational power and adaptability. They are suitable for devices where high processing power is a necessity and the tasks are data-intensive.

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    2.2.2 Memory Types and Management

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    Embedded systems utilize a variety of memory types, each fulfilling specific roles. Efficient memory management is vital for optimizing both performance and resource utilization. The following section elaborates on different types of memory and their management within the context of embedded systems:

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    • ROM (Read-Only Memory): This non-volatile memory retains data written during the manufacturing process and remains unaltered throughout the lifespan of the device. It houses firmware and boot-up instructions.

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    • RAM (Random Access Memory): This volatile memory stores transient data generated during system operation. It is faster and permits read-write operations, but data is lost when power is disconnected.

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    • Flash Memory: This is a type of non-volatile memory that can be electrically erased and reprogrammed. It is commonly used for storing firmware or data that must be retained between system reboots.

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    Memory Management:

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    • Static Memory Allocation: In this approach, memory is allocated prior to runtime and remains fixed throughout system operation.

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    • Dynamic Memory Allocation: Here, memory is allocated during runtime, offering flexibility but introducing the risk of increased complexity and potential memory leaks.

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    2.2.3 System on Chip (SoC)

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    The majority of embedded systems are Systems on Chip (SoCs). An SoC embodies an advanced level of integration technology, incorporating most components required to construct a complete system onto a single chip. It often includes a microprocessor or microcontroller, blocks of memory, peripheral interfaces, and other requisite components for a fully operational system. Below is a detailed examination of its characteristics and applications:

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    • Integration of Multiple Components: SoCs consolidate multiple components like CPUs, memory, and peripherals onto a single chip, facilitating higher levels of integration and reducing the need for external components.

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    • Power Efficiency: The high degree of integration often results in SoCs being more power-efficient compared to systems assembled from separate chips.

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    • Cost-Effectiveness: The integrated nature leads to reduced manufacturing expenses, as fewer individual components are needed.

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    • Applications: SoCs are employed in a diverse range of sectors including mobile computing, automotive electronics, and Internet of Things devices where compact form factors and energy efficiency are highly valued.

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    1. Qualcomm Snapdragon: Predominantly used in smartphones and tablets, these SoCs offer a blend of processing power, graphics, and connectivity features.

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    2. Apple A-series: Custom-developed SoCs by Apple, used in their lineup of iPhones, iPads, and in certain versions of Apple TV and HomePod. Notable examples include the A14 Bionic and A15 Bionic chips.

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    3. Samsung Exynos: Developed by Samsung, these SoCs are extensively used in their range of smartphones, tablets, and other electronic devices.

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    4. NVIDIA Tegra: Initially intended for mobile devices, these SoCs have found significant applications in automotive and gaming consoles, such as the Nintendo Switch. A visual representation can be seen below in Figure fig-soc.

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    5. Intel Atom: Employed in a wide array of systems including netbooks, smartphones, and even embedded systems, these SoCs are known for their power efficiency.

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    6. MediaTek Helio: Commonly found in budget to mid-range smartphones, these chips offer a balanced mix of power efficiency and performance.

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    7. Broadcom SoCs: Extensively used in networking equipment, Broadcom provides a variety of SoCs with diverse functionalities, including those optimized for wireless communications and data processing.

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    8. Texas Instruments (TI) OMAP: Previously popular in smartphones and tablets, these SoCs offered a range of functionalities including multimedia processing and connectivity.

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    9. Xilinx Zynq: Mainly used in embedded systems for industrial automation and in applications requiring high levels of data processing, such as advanced driver-assistance systems (ADAS).

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    10. Altera SoC FPGA: Now a part of Intel, these SoCs combine FPGA technology with ARM cores, offering flexibility and performance for a range of applications including automotive and industrial systems.

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    Figure 2.3: NVIDIA’s Tegra 2 combines two ARM Cortex-A9 cores with an ARM7 for SoC management tasks.
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    Each of these Systems on Chip (SoCs) offers a unique array of features and capabilities, tailored to meet the diverse demands of an ever-evolving technological landscape. They consolidate multiple components onto a single chip, delivering power efficiency, cost-effectiveness, and compact solutions suitable for contemporary electronic devices.

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    2.3 Embedded System Programming

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    Programming for embedded systems differs significantly from traditional software development, being specifically designed to navigate the constraints of limited resources and real-time requirements commonly associated with embedded hardware. This section aims to shed light on the distinct programming languages employed, delve into the subtleties of firmware development, and explore the pivotal role of Real-time Operating Systems (RTOS) in this specialized domain.

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    2.3.1 Programming Languages: C, C++, Python, etc.

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    Choosing the right programming languages is essential in embedded systems, often emphasizing direct hardware interaction and memory usage optimization. Here, we will examine the unique attributes of these languages and how they differ from those commonly used in more conventional computing systems:

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    • C: Often considered the bedrock of embedded systems programming, the C language enables direct engagement with hardware, providing capabilities for bit-wise operations and memory address manipulation. Its procedural nature and low-level functionalities make it the preferred choice for resource-constrained environments, particularly for firmware development.

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    • C++: Building upon the foundational principles of C, C++ incorporates object-oriented features, promoting organized and modular code development. Despite its inherent complexity, it is employed in scenarios where higher-level abstractions do not undermine the detailed control offered by C.

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    • Python: Although not a traditional choice for embedded systems due to its higher memory consumption and runtime delays, Python is gradually gaining traction in the embedded sphere, particularly in systems with less stringent resource limitations. A specialized variant known as MicroPython has been developed, optimized for microcontrollers and retaining the simplicity and ease of Python. This flexible programming paradigm facilitates quick prototyping and development, as illustrated by the code snippet below that interfaces with pins on a PyBoard.

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    Comparison with Traditional Systems: In contrast to mainstream computing systems, where languages like Java, Python, or JavaScript are lauded for their ease of development and extensive libraries, embedded systems favor languages that provide fine-grained control over hardware and opportunities for optimization, all while carefully navigating resource constraints.

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    Firmware development in embedded systems involves creating programs that are permanently stored in the device’s non-volatile memory, ensuring consistent operation. This section outlines how firmware development diverges from software development in traditional computing systems:

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    1. Resource Optimization: The imperative for continual optimization is paramount, enabling the code to operate within the limitations of restricted memory and processing capabilities.

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    2. Hardware Interaction: Firmware often maintains a close relationship with hardware, requiring an in-depth understanding of hardware components and their functionalities.

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    3. Lifecycle Management: Firmware updates are less frequent than software updates in traditional systems, necessitating rigorous testing to prevent failures that could lead to hardware malfunctions.

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    4. Security Concerns: Given its integral role, firmware is a potential target for security breaches, necessitating meticulous attention to security aspects, including secure coding practices and encryption protocols.

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    RTOSs serve as the backbone for real-time embedded systems, managing task execution in a predictable and deterministic manner. This is a marked departure from operating systems in general-purpose computing, as outlined below:

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    1. Deterministic Timing: RTOSs are designed to respond to inputs or events within a well-defined time frame, fulfilling the stringent time-sensitive requirements of many embedded systems.

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    2. Task Prioritization: These systems enable task prioritization, allowing critical tasks to receive preferential processing time over less crucial tasks.

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    3. Microkernel Architecture: Many RTOSs employ a microkernel architecture, epitomizing efficiency and minimalism by focusing solely on essential functionalities.

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    4. Memory Management: Memory management in RTOSs is often more streamlined compared to their counterparts in traditional operating systems, contributing to quick response times and operational efficiency.

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    Examples of RTOS: Notable instances in this category include FreeRTOS, RTEMS, and VxWorks, each providing unique features tailored to meet the varied needs of different embedded systems applications.

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    Embedded systems engage with the external environment through a range of interfaces and peripherals, which are often more specialized and streamlined than those in general-purpose systems. Let us explore these in detail:

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    2.4.1 Digital I/O

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    Digital Input/Output (I/O) interfaces are fundamental to embedded systems, enabling interaction with other devices and components. For instance, a digital I/O pin may be used to read a binary signal (0 or 1) from sensors or to control actuators. In embedded systems, these I/O ports often operate under strict timing constraints, a

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    requirement less common in general-purpose computing systems. Moreover, these systems are usually programmed for specific, optimized operations on digital signals, sometimes needing to function in real-time or near-real-time settings.

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    2.4.2 Analog Interfaces

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    Analog interfaces in embedded systems are vital for interacting with a predominantly analog world. These interfaces may include components like Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). For example, ADCs can be employed to read sensor data from environmental sensors such as temperature or humidity sensors, converting real-world analog data into a digital format that the microcontroller can process.

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    In contrast to general-purpose systems, embedded systems often utilize analog interfaces more directly and frequently, especially in sensor-integrated applications that require the conversion of a broad range of analog signals into digital data for further processing and analysis.

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    If you examine Figure fig-nicla-io closely, you will notice indications of I/O pinouts for analog, digital, and communication layouts.

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    Figure 2.4: Nicla Vision pinout
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    2.4.3 Communication Protocols (SPI, I2C, UART, etc.)

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    Communication protocols act as the channels that enable communication between various components within or connected to an embedded system. Let us examine some commonly used ones:

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    • SPI (Serial Peripheral Interface): This synchronous serial communication protocol is primarily used for short-distance communication in embedded systems. For instance, it is frequently employed in communications with SD cards and TFT displays.

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    • I2C (Inter-Integrated Circuit): This multi-master, multi-slave, packet-switched, single-ended, serial communication bus is widely used in embedded systems to connect low-speed peripherals to motherboards, embedded systems, or cell phones. It is valued for its simplicity and low pin count.

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    • UART (Universal Asynchronous Receiver-Transmitter): This protocol enables asynchronous serial communication between devices. It is commonly used in embedded systems to transmit data between devices over a serial port, such as sending data logs from a sensor node to a computer.

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    Compared to general-purpose systems, communication protocols in embedded systems are often more finely tuned for speed and reliability, especially in critical applications where data transmission integrity is crucial. Additionally, these protocols may be directly integrated into the microcontroller, facilitating more cohesive and seamless interactions between components, a feature less commonly observed in general-purpose systems.

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    2.5 Power Management in Embedded Systems

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    Power management is a critical focus area in the design of embedded systems, influencing both the system’s efficiency and its applicability in real-world scenarios. The wide range of applications for embedded systems, from handheld devices to industrial equipment, highlights the need for meticulous power management. Let us explore this essential aspect of embedded systems:

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    2.5.1 Power Consumption Considerations

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    In embedded systems, power consumption is a key factor that dictates both performance and longevity. Microcontrollers in these systems usually operate within a voltage range of 1.8V to 5V, with current consumption varying from microamperes (μA) to milliamperes (mA) during active states. In sleep or standby modes, the current consumption can drop to nanoamperes (nA), extending battery life.

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    In contrast, general-purpose computing systems like desktop computers consume power on the scale of tens to hundreds of watts, several orders of magnitude higher than embedded systems. This significant difference underscores the need for careful power management in embedded systems, where the power budget is often much more limited.

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    Managing power consumption involves a complex interplay of factors such as operating voltage, clock frequency, and the specific tasks the system performs. Engineers often find themselves balancing power consumption against performance and responsiveness, navigating a complex landscape of trade-offs.

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    2.5.2 Energy-Efficient Design

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    Incorporating energy efficiency into the design phase is crucial for the successful deployment of embedded systems. Techniques like dynamic voltage and frequency scaling (DVFS) are often employed, allowing the system to adjust voltage and frequency dynamically based on processing needs, thereby optimizing power consumption.

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    Additionally, the use of low-power modes, where non-essential peripherals are deactivated or clock frequencies are reduced, can significantly conserve energy. For example, deep sleep modes that consume as little as 100 nA can dramatically extend battery life, particularly in battery-operated embedded systems.

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    The architecture of the microcontroller, especially its instruction set architecture (ISA), is often highly specialized to eliminate unnecessary complexities that could increase power consumption. This specialization allows operations to be executed in fewer cycles compared to general-purpose processors, reducing the power consumed per operation. Moreover, these specialized ISAs are designed to efficiently execute the specific tasks that the embedded system is intended to perform, optimizing the execution path and thereby saving energy.

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    2.5.3 Battery Management

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    Managing batteries is an integral component of power management strategies in embedded systems. The goal is to maximize battery life without sacrificing performance. Battery-powered embedded systems often use lithium-ion or lithium-polymer batteries due to their high energy density and rechargeable features. These batteries typically have a voltage range of 3.7V to 4.2V per cell. For example, the Nicla Vision utilizes a 3.7V battery, as shown in Figure fig-battery.

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    Figure 2.5: Nicla Vision battery
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    By focusing on these elements, engineers can create systems that not only meet functional requirements but do so in a manner that reflects a deep understanding of the broader impacts of technology on society and the environment.

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    Engineers are tasked with implementing methods such as effective charge regulation, protection against voltage spikes, and thermal monitoring to ensure the longevity and health of the battery. Additionally, the incorporation of systems that can tap into renewable energy sources like solar or kinetic energy can augment battery reserves, leading to enduring and eco-friendly solutions.

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    The emphasis on power management is driven by the imperative to make the most of available resources, prolong battery longevity, and minimize operational expenditures. In scenarios where the embedded systems are situated in remote or hard-to-reach locations, adept power management can substantially cut down on the frequency of maintenance visits, thereby guaranteeing continuous and seamless functionality.

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    It’s fair to assert that power management goes beyond being a mere technical specification in embedded systems; it serves as a pivotal factor that can either make or break the success of a project. Significant engineering effort is channeled into fine-tuning power management approaches, aiming to develop systems that are not just operationally efficient but also environmentally sustainable. This reflects a profound dedication to both technological innovation and excellence within the realm of embedded systems.

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    2.6 Real-Time Characteristics

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    Within the complex tapestry of embedded systems, real-time attributes serve as essential threads, interlacing various components and tasks into a unified, responsive whole. This element, often specific to embedded systems, occupies a vital role in both their architecture and functionality, endowing them with the nimbleness and accuracy needed for timely interaction with their surroundings. Let’s examine the nuances that underscore the real-time attributes of embedded systems:

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    2.6.1 Real-time Clocks

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    Real-time clocks (RTCs) hold a central position in embedded systems, offering an accurate time benchmark that directs the system’s activities. These clocks frequently come with battery backups to maintain reliable timekeeping, even when the primary power source is compromised. The role of RTCs is more critical and widespread in embedded systems compared to general-purpose computing, where timekeeping, while important, usually doesn’t govern the core operations of the system.

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    For example, in the realm of industrial automation, RTCs facilitate the precise coordination of tasks, ensuring synchronized and timely processes. They are particularly crucial in scenarios requiring time-stamped data, such as environmental monitoring systems where the accuracy and time relevance of data are imperative.

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    2.6.2 Timing and Synchronization

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    Timing and synchronization stand as defining features of embedded systems, requiring various components and processes to operate in concert. The essence of a real-time embedded system is shaped by its capability to execute tasks within a specified time window. Such systems often have rigorous timing constraints, necessitating synchronization methods that are both sturdy and exact.

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    In the context of automotive control systems, the synchronized and timely operation of diverse sensors and actuators is imperative for both safety and peak performance. This sharply contrasts with general-purpose systems, where timing, though managed, usually lacks immediate and critical consequences.

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    2.6.3 Task Management and Scheduling

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    In the world of embedded systems, the management and scheduling of tasks are crucial for effective real-time responses. Task schedulers in these systems often use techniques like priority scheduling, where tasks are ranked by importance, allowing higher-priority tasks to interrupt those of lower priority. This is especially critical in systems where some functions have greater urgency.

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    For example, in medical devices such as pacemakers, the punctual delivery of electrical impulses is a high-priority task, and the scheduler must give it precedence over all other activities to ensure patient safety. This level of refined scheduling and task management sets embedded systems apart from the more adaptable but less deterministic scheduling seen in general-purpose systems.

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    2.6.4 Error Handling and Fault Tolerance

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    To enhance their real-time features, embedded systems frequently incorporate mechanisms for error detection and fault resilience. These are engineered to swiftly identify and rectify errors or to sustain system functionality even when faults occur. In aviation control systems, for instance, real-time fault tolerance is essential for maintaining the stability and safety of drones. This meticulous approach to error management is somewhat unique to embedded systems, accentuating the critical nature of many such applications.

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    The real-time attributes of embedded systems distinguish them, creating an environment where accuracy, synchrony, and prompt responses are not optional but obligatory. These attributes resonate across a wide range of applications, from automotive systems to industrial automation and healthcare devices, highlighting the role of embedded systems as quiet yet potent conductors of a technologically synchronized world. Through their real-time features, embedded systems offer solutions that not only satisfy functional needs but do so with a degree of precision and dependability that is both extraordinary and essential in today’s world.

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    2.7 Security and Reliability

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    In an increasingly interconnected and tech-dependent world, the issues of security and reliability have risen to become primary considerations in system engineering. This is especially true for embedded systems, which often serve as key components in critical infrastructures and applications, thereby raising the stakes considerably. Let’s explore the crucial elements that fortify the bastion of security and reliability in embedded systems:

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    2.7.1 Secure Boot and Root of Trust

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    Embedded systems are increasingly central to a variety of critical applications, making it imperative to assure their authenticity and integrity from the moment they boot up. The secure boot sequence serves as a foundational element in this security framework, permitting the system to run only code that has been authenticated and deemed trustworthy. This is often augmented by a “Root of Trust,” a stable and secure environment, typically hardware-based, that validates the initial firmware and each subsequent layer of software during the boot-up sequence.

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    For example, in financial settings involving Point-of-Sale (POS) terminals, a secure boot mechanism guarantees that the firmware remains intact and secure, thereby preventing any malicious alterations that could lead to significant data breaches. Likewise, in the realm of home automation, a strong secure boot process acts as a barrier to unauthorized access, thereby protecting user data and privacy.

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    2.7.2 Fault Tolerance

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    Fault tolerance is an essential quality in embedded systems, granting them the ability to maintain functionality even when faced with faults or system failures. This resilience is achieved through various means such as redundancy, where vital components are replicated to assume control in the event of a failure, or via sophisticated error detection and correction methods.

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    In sectors like aerospace and aviation, fault tolerance is not merely an advantageous feature but an obligatory specification. For instance, aircraft control systems utilize multiple redundant configurations that operate in parallel to assure uninterrupted functionality, even if a component fails. This degree of fault tolerance provides a heightened level of reliability, enabling the system to endure failures without disastrous outcomes, a feature that distinguishes it from conventional computing systems.

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    2.7.3 Safety-Critical Systems

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    Safety-critical systems are defined as those where a malfunction could lead to loss of life, substantial property damage, or environmental degradation. Such systems demand rigorous design protocols to guarantee the highest levels of reliability and safety. Embedded systems falling under this classification often comply with stringent development guidelines and are subject to exhaustive testing to confirm their safety and reliability metrics.

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    For instance, in automotive safety features like Anti-lock Braking Systems (ABS) and Electronic Stability Control (ESC), embedded controllers are crucial. These controllers are engineered in accordance with rigorous standards like ISO 26262, ensuring they meet the elevated safety and reliability criteria essential for safeguarding lives. In the healthcare sector, devices such as pacemakers and infusion pumps are categorized as safety-critical, where the dependability of embedded systems can quite literally be life-altering.

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    The focus on security and reliability in embedded systems is of paramount importance, a point that is often underestimated by many. As these systems become increasingly woven into the fabric of our everyday lives and critical infrastructure, the principles of security and reliability serve as guiding lights in their development and deployment. Through features like secure booting and fault tolerance, these systems offer not just operational efficiency but also a layer of trust and security, providing a steadfast and secure anchor in a rapidly evolving technological landscape. These foundational tenets shape today’s embedded systems, molding them into dependable stewards and proficient operators in various critical domains of contemporary society.

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    2.8 Future Trends and Challenges

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    Arm, the leading producer of microcontrollers, has reached a milestone by shipping an unprecedented 8.0 billion chips, either directly or through its partners. This takes the total number of chips shipped to date to an astounding quarter of a trillion, or 250 billion (ARM.com)!

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    -ARM.com. “The Future Is Being Built on Arm: Market Diversification Continues to Drive Strong Royalty and Licensing Growth as Ecosystem Reaches Quarter of a Trillion Chips Milestone – Arm®.” https://www.arm.com/company/news/2023/02/arm-announces-q3-fy22-results. -

    As we find ourselves at the threshold of a new era marked by extraordinary growth in the embedded systems sector, it becomes both exhilarating and imperative to scrutinize the emerging trends and challenges that lie ahead. From the expanding horizons of edge computing to the imperatives of scalability, the landscape is poised for transformation, unveiling new realms of both opportunities and challenges. Let’s explore the evolving frontier that awaits embedded systems:

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    2.8.1 Edge Computing and IoT

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    With the rapid expansion of the Internet of Things (IoT), edge computing is gaining increasing prominence. Essentially, edge computing enables data to be processed closer to its source, thereby reducing latency and alleviating the burden on centralized data centers. This shift in computing paradigms is anticipated to revolutionize embedded systems, endowing them with enhanced processing power and the intelligence to perform intricate tasks on-site.

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    Additionally, as the IoT is projected to include billions of interconnected devices worldwide, embedded systems are slated to be the linchpin in ensuring smooth connectivity and interoperability among a diverse set of devices. This interconnected ecosystem is expected to enable real-time analytics and decision-making, laying the groundwork for more intelligent cities, industries, and households. The challenge resides in crafting systems that are secure, energy-efficient, and adept at managing the anticipated data deluge effectively.

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    2.8.2 Scalability and Upgradation

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    As the landscape of embedded systems continues its evolutionary trajectory, the focus will increasingly turn towards scalability and ease of upgradation. Systems will be required to adapt to evolving technologies and user needs without undergoing extensive modifications. This necessitates modular architectures and adherence to open standards, facilitating the effortless incorporation of new functionalities and features.

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    Moreover, in light of rapid technological advancements, embedded systems will need to incorporate capabilities for remote updates and maintenance to ensure their continued relevance and longevity. The responsibility will fall on the shoulders of developers and manufacturers to engineer systems that not only satisfy current needs but are also prepared for future enhancements, thereby securing a path for sustainable and progressive development.

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    2.8.3 Market Opportunities

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    The market landscape for embedded systems is on the cusp of dynamic changes. As various industries accelerate their adoption of automation and digital transformation, the demand for advanced embedded systems is set to skyrocket. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into embedded systems is expected to offer unparalleled levels of intelligence and automation.

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    At the same time, burgeoning opportunities are emerging in sectors like consumer electronics, automotive, healthcare, and industrial applications. While this growth presents enormous potential for innovation, it also introduces challenges such as heightened competition and the necessity for adherence to evolving regulatory frameworks. Companies entering this arena will need to exhibit agility, innovation, and adaptability to the shifting market conditions in order to establish a competitive edge.

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    2.9 Conclusion

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    The table provides a side-by-side comparison between these two distinct types of computing systems, covering a range of categories including processing power, memory capabilities, user interface, and real-time functionalities, among others. The aim of this comparative analysis is to offer readers a concise yet thorough understanding of the unique attributes and specificities of both conventional and embedded computing systems. This, in turn, enables a more nuanced and informed grasp of their respective roles in today’s computing landscape.

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    CategoryTraditional Computing SystemEmbedded System Architecture
    Hardware Characteristics
    Processing PowerHigh (Multi-core processors)Moderate to Low (Single/Multi-core, optimized for specific tasks)
    MemoryHigh (Upgradable)Limited (Fixed)
    StorageHigh (Upgradable)Limited (Fixed or expandable to a certain extent)
    Hardware ScalabilityHigh (Can upgrade various components)Low (Hardware is often fixed and focused)
    Software Characteristics
    Operating SystemGeneral Purpose (Windows, Linux, macOS)Real-Time Operating System (RTOS) or No OS
    Development FlexibilityHigh (Supports multiple programming languages and frameworks)Moderate (Focused on specific programming languages and tools)
    Performance & Efficiency
    Power ConsumptionHighLow (Optimized for energy efficiency)
    Real-Time CapabilitiesLimited (Not optimized for real-time tasks)High (Designed for real-time tasks)
    User Interaction
    User InterfaceComplex (GUI-Based)Simple or None (Can be GUI, command-line, or none)
    ConnectivityExtensive (Multiple ports and connectivity options)Limited (Focused on necessary connectivity options)
    Lifecycle & Maintenance
    MaintenanceRegular Maintenance RequiredLow Maintenance (Set up to run specific tasks consistently)
    LifecycleShorter (Due to rapid technological advancements)Longer (Designed to perform specific tasks over a long period)
    Cost and Use Cases
    CostVariable (Can be high depending on specifications)Generally Lower (Due to focused functionalities)
    Use CasesGeneral (Various applications across sectors)Specific (Dedicated to particular tasks or applications)
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    As we gaze into the future, it’s clear that the realm of embedded systems stands on the cusp of a transformative era, characterized by groundbreaking innovations, abundant opportunities, and formidable challenges. The horizon is replete with the promise of enhanced connectivity, heightened intelligence, and superior efficiency, carving out a trajectory where embedded systems will serve as the guiding force behind society’s technological progress. The path forward is one of discovery and adaptability, where the confluence of technological prowess and creative ingenuity will sculpt a future that is not only rich in technological advancements but also attuned to the intricate and continually shifting needs of a dynamic global landscape. It’s a field teeming with possibilities, inviting trailblazers to embark on a journey to define the parameters of a bright and flourishing future.

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    2.10 Exercises

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    Now would be a great time for you to get your hands on a real embedded device, and get it setup.

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    If you want to play with an embedded system, try out the Nicla Vision

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    Setup Nicla Vision

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    FRONT MATTER

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    MACHINE LEARNING SYSTEMS

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    for TinyML

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    Abstract
    - Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems. -
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    Preface

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    Welcome to “Machine Learning Systems for TinyML” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.

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    Our aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.

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    “If you want to go fast, go alone. If you want to go far, go together.” – African Proverb

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    This isn’t just a static textbook; it’s a living, breathing document. We’re making it open-source and continually updated to meet the ever-changing needs of this dynamic field. Expect a rich blend of expert knowledge that guides you through the complex interplay between cutting-edge algorithms and the foundational principles that make them work. We’re setting the stage for the next big leap in tech innovation.

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    Why We Wrote This Book

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    We’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Machine Learning Systems for TinyML.” We’re going beyond the traditional textbook model to create a living knowledge hub.

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    The book covers principles, algorithms, and real-world application case studies, aiming to give you a deep understanding that will help you navigate the ever-changing landscape of embedded AI. By keeping it open, we’re not just making learning accessible; we’re inviting new ideas and ongoing improvements. In short, we’re building a community where knowledge is free to grow and light the way forward in global embedded AI tech.

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    What You’ll Need to Know

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    Don’t worry, you don’t need to be a machine learning whiz to dive into this book. All you really need is a basic understanding of systems and a curiosity to explore how embedded hardware, AI, and software come together. This is where innovation happens, and a basic grasp of how systems work will be your compass.

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    We’re also focusing on the exciting overlaps between these fields, aiming to create a learning environment where traditional boundaries fade away, making room for a more holistic, integrated view of modern tech. Your interest in embedded AI and low-level software will guide you through a rich and rewarding learning experience.

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    Book Conventions

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    For details on the conventions used in this book, check out the Conventions section.

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    Want to Help Out?

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    If you’re interested in contributing, you can find the guidelines here.

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    Get in Touch

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    Got questions or feedback? Feel free to e-mail us.

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    Contributors

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    A big thanks to everyone who’s helped make this book what it is! You can see the full list of contributors here.

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    1  Introduction

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    1.1 Overview

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    Welcome to this comprehensive exploration of Tiny Machine Learning (TinyML). This book aims to bridge the gap between intricate machine learning theories and their practical applications on small devices. Whether you’re a newcomer, an industry professional, or an academic researcher, this book offers a balanced mix of essential theory and hands-on insights into TinyML.

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    1.2 What’s Inside

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    The book starts with a foundational look at embedded systems and machine learning, focusing on deep learning methods due to their effectiveness across various tasks. We then guide you through the entire machine learning workflow, from data engineering to advanced model training.

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    We also delve into TinyML model optimization and deployment, with a special emphasis on on-device learning. You’ll find comprehensive discussions on current hardware acceleration techniques and model lifecycle management. Additionally, we explore the sustainability and ecological impact of AI, and how TinyML fits into this larger conversation.

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    The book concludes with a look at the exciting possibilities of generative AI within the TinyML context.

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    1.3 Chapter Breakdown

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    Here’s a closer look at what each chapter covers:

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    Chapter 1: Introduction This chapter sets the stage, providing an overview of embedded AI and laying the groundwork for the chapters that follow.

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    Chapter 2: Embedded Systems We introduce the basics of embedded systems, the platforms where AI algorithms are widely applied.

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    Chapter 3: Deep Learning Primer This chapter offers a comprehensive introduction to the algorithms and principles that underpin AI applications in embedded systems.

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    Chapter 4: Embedded AI Here, we explore how machine learning techniques can be integrated into embedded systems, enabling intelligent functionalities.

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    Chapter 5: AI Workflow This chapter breaks down the machine learning workflow, offering insights into the steps leading to proficient AI applications.

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    Chapter 6: Data Engineering We focus on the importance of data in AI systems, discussing how to effectively manage and organize data.

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    Chapter 7: AI Training This chapter delves into model training, exploring techniques for developing efficient and reliable models.

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    Chapter 8: On-Device AI Here, we discuss strategies for achieving efficiency in AI applications, from computational resource optimization to performance enhancement.

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    Chapter 9: Model Optimizations We explore various avenues for optimizing AI models for seamless integration into embedded systems.

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    Chapter 10: AI Frameworks This chapter reviews different frameworks for developing machine learning models, guiding you in choosing the most suitable one for your projects.

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    Chapter 11: AI Acceleration We discuss the role of specialized hardware in enhancing the performance of embedded AI systems.

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    Chapter 12: Benchmarking AI This chapter focuses on how to evaluate AI systems through systematic benchmarking methods.

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    Chapter 13: On-Device Learning We explore techniques for localized learning, which enhances both efficiency and privacy.

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    Chapter 14: Embedded AIOps This chapter looks at the processes involved in the seamless integration, monitoring, and maintenance of AI functionalities in embedded systems.

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    Chapter 15: Privacy and Security As AI becomes more ubiquitous, this chapter addresses the crucial aspects of privacy and security in embedded AI systems.

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    Chapter 16: Responsible AI We discuss the ethical principles guiding the responsible use of AI, focusing on fairness, accountability, and transparency.

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    Chapter 17: AI Sustainability This chapter explores practices and strategies for sustainable AI, ensuring long-term viability and reduced environmental impact.

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    Chapter 18: Generative AI We explore the algorithms and techniques behind generative AI, opening avenues for innovation and creativity.

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    Chapter 19: AI for Good

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    We highlight positive applications of TinyML in areas like healthcare, agriculture, and conservation.

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    To get the most out of this book, consider the following structured approach:

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    1. Foundational Knowledge (Chapters 1-4): Start by building a strong foundation with the initial chapters, which provide the context and groundwork for more advanced topics.

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    2. Practical Insights (Chapters 5-14): With a solid foundation, move on to the chapters that offer practical insights into machine learning workflows, data engineering, and optimizations. Engage in hands-on exercises and case studies to solidify your understanding.

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    3. Ethics and Sustainability (Chapters 15-17): These chapters offer a critical perspective on the ethical and sustainable practices in AI, encouraging responsible AI deployment.

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    4. Future Trends (Chapter 18): Conclude your journey by exploring the exciting domain of generative AI, which offers a glimpse into the future of the field.

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    5. Interconnected Learning: While the chapters are designed for a progressive learning curve, feel free to navigate non-linearly based on your interests and needs.

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    6. Practical Applications: Throughout the book, try to relate theoretical knowledge to real-world applications. Engage with practical exercises and case studies to bridge the gap between theory and practice.

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    7. Discussion and Networking: Engage in discussions, forums, or study groups to share insights and debate concepts, which can deepen your understanding.

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    8. Revisit and Reflect: Given the dynamic nature of AI, don’t hesitate to revisit chapters. A second reading can offer new insights and foster continuous learning.

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    By adopting this structured yet flexible approach, you’re setting the stage for a fulfilling and enriching learning experience.

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    1.5 The Road Ahead

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    As we navigate the multifaceted world of embedded AI, we’ll cover a broad range of topics, from computational theories and engineering principles to ethical considerations and innovative applications. Each chapter unveils a piece of this expansive puzzle, inviting you to forge new connections, ignite discussions, and fuel a perpetual curiosity about embedded AI. Join us as we explore this fascinating field, which is not only reshaping embedded systems but also redrawing the contours of our technological future.

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    1.6 Contribute Back

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    Learning in the fast-paced world of embedded AI is a collaborative journey. This book aims to nurture a vibrant community of learners, innovators, and contributors. As you explore the concepts and engage with the exercises, we encourage you to share your insights and experiences. Whether it’s a novel approach, an interesting application, or a thought-provoking question, your contributions can enrich the learning ecosystem. Engage in discussions, offer and seek guidance, and collaborate on projects to foster a culture of mutual growth and learning. By sharing knowledge, you play a pivotal role in fostering a globally connected, informed, and empowered community.

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    Edited by Prof. Vijay Janapa Reddi (Harvard University)
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    This book was built with Quarto.
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    - - - - \ No newline at end of file diff --git a/optimizations.qmd b/optimizations.qmd index 78b35758..6f815a59 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -43,7 +43,7 @@ So how does one choose the type of pruning methods? Many variations of pruning t #### Structured Pruning -We start with structured pruning, a technique that reduces the size of a neural network by eliminating entire model-specific substructures while maintaining the overall model structure. It removes entire neurons/filters or layers based on importance criteria. For example, for a convolutional neural network (CNN), this could be certain filter instances or channels. For fully connected networks, this could be neurons themselves while maintaining full connectivity or even be elimination of entire model layers that are deemed to be insignificant. This type of pruning often leads to regular, structured sparse networks that are hardware friendly. +We start with structured pruning, a technique that reduces the size of a neural network by eliminating entire model-specific substructures while maintaining the overall model structure. It removes entire neurons/channels or layers based on importance criteria. For example, for a convolutional neural network (CNN), this could be certain filter instances or channels. For fully connected networks, this could be neurons themselves while maintaining full connectivity or even be elimination of entire model layers that are deemed to be insignificant. This type of pruning often leads to regular, structured sparse networks that are hardware friendly. ##### Components @@ -57,7 +57,7 @@ Best practices have started to emerge on how to think about structured pruning. Given that there are different strategies, each of these structures (i.e., neurons, channels and layers) is pruned based on specific criteria and strategies, ensuring that the reduced model maintains as much of the predictive prowess of the original model as possible while gaining in computational efficiency and reduction in size. -The primary structures targeted for pruning include **neurons** , channels, and sometimes, entire layers, each having its unique implications and methodologies. When neurons are pruned, we are removing entire neurons along with their associated weights and biases, thereby reducing the width of the layer. This type of pruning is often utilized in fully connected layers. +The primary structures targeted for pruning include **neurons**, channels, and sometimes, entire layers, each having its unique implications and methodologies. When neurons are pruned, we are removing entire neurons along with their associated weights and biases, thereby reducing the width of the layer. This type of pruning is often utilized in fully connected layers. With **channel** pruning, which is predominantly applied in convolutional neural networks (CNNs), it involves eliminating entire channels or filters, which in turn reduces the depth of the feature maps and impacts the network's ability to extract certain features from the input data. This is particularly crucial in image processing tasks where computational efficiency is paramount. @@ -108,7 +108,7 @@ The pruned model, while smaller, retains its original architectural form, which Unstructured pruning is, as its name suggests, pruning the model without regard to model-specific substructure. As mentioned above, it offers a greater aggression in pruning and can achieve higher model sparsities while maintaining accuracy given less constraints on what can and can't be pruned. Generally, post-training unstructured pruning consists of an importance criterion for individual model parameters/weights, pruning/removal of weights that fall below the criteria, and optional fine-tuning after to try and recover the accuracy lost during weight removal. -Unstructured pruning has some advantages over structured pruning: removing individual weights instead of entire model substructures often leads in practice to lower model accuracy hits. Furthermore, generally determining the criterion of importance for an individual weight is much simpler than for an entire substructure of parameters in structured pruning, making the former preferable for cases where that overhead is hard or unclear to compute. Similarly, the actual process of structured pruning is generally less flexible, as removing individual weights is generally simpler than removing entire substructures and ensuring the model still works. +Unstructured pruning has some advantages over structured pruning: removing individual weights instead of entire model substructures often leads in practice to lower model accuracy decreases. Furthermore, generally determining the criterion of importance for an individual weight is much simpler than for an entire substructure of parameters in structured pruning, making the former preferable for cases where that overhead is hard or unclear to compute. Similarly, the actual process of structured pruning is generally less flexible, as removing individual weights is generally simpler than removing entire substructures and ensuring the model still works. Unstructured pruning, while offering the potential for significant model size reduction and enhanced deployability, brings with it challenges related to managing sparse representations and ensuring computational efficiency. It is particularly useful in scenarios where achieving the highest possible model compression is paramount and where the deployment environment can handle sparse computations efficiently. @@ -138,7 +138,7 @@ More formally, the lottery ticket hypothesis is a concept in deep learning that #### Challenges & Limitations -There is no free lunch with pruning optimizations. +There is no free lunch with pruning optimizations, with some choices coming with wboth improvements and costs to considers. Below we discuss some tradeoffs for practitioners to consider. ##### Quality vs. Size Reduction @@ -198,11 +198,11 @@ These challenges underscore the necessity for a thorough and nuanced approach to #### Low-rank Matrix Factorization -Similar in approximation theme, low-rank matrix factorization (LRFM) is a mathematical technique used in linear algebra and data analysis to approximate a given matrix by decomposing it into two or more lower-dimensional matrices. The fundamental idea is to express a high-dimensional matrix as a product of lower-rank matrices, which can help reduce the complexity of data while preserving its essential structure. Mathematically, given a matrix $A \in \mathbb{R}^{m \times n}$, LRMF seeks matrices $U \in \mathbb{R}^{m \times k}$ and $V \in \mathbb{R}^{k \times n}$ such that $A \approx UV$, where $k$ is the rank and is typically much smaller than $m$ and $n$. +Similar in approximation theme, low-rank matrix factorization (LRMF) is a mathematical technique used in linear algebra and data analysis to approximate a given matrix by decomposing it into two or more lower-dimensional matrices. The fundamental idea is to express a high-dimensional matrix as a product of lower-rank matrices, which can help reduce the complexity of data while preserving its essential structure. Mathematically, given a matrix $A \in \mathbb{R}^{m \times n}$, LRMF seeks matrices $U \in \mathbb{R}^{m \times k}$ and $V \in \mathbb{R}^{k \times n}$ such that $A \approx UV$, where $k$ is the rank and is typically much smaller than $m$ and $n$. ##### Background and Benefits -One of the seminal works in the realm of matrix factorization, particularly in the context of recommendation systems, is the paper by Yehuda Koren, Robert Bell, and Chris Volinsky, ["Matrix Factorization Techniques for Recommender Systems" (2009)]([https://ieeexplore.ieee.org/document/5197422](https://ieeexplore.ieee.org/document/5197422)). The authors delve into various factorization models, providing insights into their efficacy in capturing the underlying patterns in the data and enhancing predictive accuracy in collaborative filtering. LRFM has been widely applied in recommendation systems (such as Netflix, Facebook, etc.), where the user-item interaction matrix is factorized to capture latent factors corresponding to user preferences and item attributes. +One of the seminal works in the realm of matrix factorization, particularly in the context of recommendation systems, is the paper by Yehuda Koren, Robert Bell, and Chris Volinsky, ["Matrix Factorization Techniques for Recommender Systems" (2009)]([https://ieeexplore.ieee.org/document/5197422](https://ieeexplore.ieee.org/document/5197422)). The authors delve into various factorization models, providing insights into their efficacy in capturing the underlying patterns in the data and enhancing predictive accuracy in collaborative filtering. LRMF has been widely applied in recommendation systems (such as Netflix, Facebook, etc.), where the user-item interaction matrix is factorized to capture latent factors corresponding to user preferences and item attributes. The main advantage of low-rank matrix factorization lies in its ability to reduce data dimensionality as shown in the image below where there are fewer parameters to store, making it computationally more efficient and reducing storage requirements at the cost of some additional compute. This can lead to faster computations and more compact data representations, which is especially valuable when dealing with large datasets. Additionally, it may aid in noise reduction and can reveal underlying patterns and relationships in the data. @@ -212,7 +212,7 @@ The main advantage of low-rank matrix factorization lies in its ability to reduc But practitioners and researchers encounter a spectrum of challenges and considerations that necessitate careful attention and strategic approaches. As with any lossy compression technique, we may lose information during this approximation process: choosing the correct rank that balances the information lost and the computational costs is tricky as well and adds an additional hyper-parameter to tune for. -Low-rank matrix factorization is a valuable tool for dimensionality reduction and making compute fit onto edge devices but, like other techniques, needs to be carefully tuned to the model and task at hand. A key challenge resides in managing the computational complexity inherent to LRMF, especially when grappling with high-dimensional and large-scale data. The computational burden, particularly in the context of real-time applications and massive datasets, remains a significant hurdle for effectively using LRFM. +Low-rank matrix factorization is a valuable tool for dimensionality reduction and making compute fit onto edge devices but, like other techniques, needs to be carefully tuned to the model and task at hand. A key challenge resides in managing the computational complexity inherent to LRMF, especially when grappling with high-dimensional and large-scale data. The computational burden, particularly in the context of real-time applications and massive datasets, remains a significant hurdle for effectively using LRMF. Moreover, the conundrum of choosing the optimal rank, \(k\), for the factorization introduces another layer of complexity. The selection of \(k\) inherently involves a trade-off between approximation accuracy and model simplicity, and identifying a rank that adeptly balances these conflicting objectives often demands a combination of domain expertise, empirical validation, and sometimes, heuristic approaches. The challenge is further amplified when the data encompasses noise or when the inherent low-rank structure is not pronounced, making the determination of a suitable \(k\) even more elusive. @@ -224,13 +224,13 @@ Furthermore, in scenarios where data evolves or grows over time, developing LRMF Similar to low-rank matrix factorization, more complex models may store weights in higher dimensions, such as tensors: tensor decomposition is the higher-dimensional analogue of matrix factorization, where a model tensor is decomposed into lower rank components, which again are easier to compute on and store but may suffer from the same issues as mentioned above of information loss and nuanced hyperparameter tuning. Mathematically, given a tensor $\mathcal{A}$, tensor decomposition seeks to represent $\mathcal{A}$ as a combination of simpler tensors, facilitating a compressed representation that approximates the original data while minimizing the loss of information. -The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Applications"](https://epubs.siam.org/doi/abs/10.1137/07070111X) (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments . +The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Applications"](https://epubs.siam.org/doi/abs/10.1137/07070111X) (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments. ![A visualization showing the decrease in parameterization enabled by tensor decomposition. Observe how the shown three-dimensional tensor $y$ can be approximated by three matrices, all of lower rank, greatly reducing the number of parameters to be held in memory (@chen__inpainting_2022).](images/modeloptimization_tensor_decomposition.png) ### Edge-Aware Model Design -Finally, we reach the other end of the gradient, where we specifically make model architecture decisions directly given knowledge of the edge devices we wish to deploy on. +Finally, we reach the other end of the hardware-software gradient, where we specifically make model architecture decisions directly given knowledge of the edge devices we wish to deploy on. As covered in previous sections, edge devices are constrained specifically with limitations on memory and parallelizable computations: as such, if there are critical inference speed requirements, computations must be flexible enough to satisfy hardware constraints, something that can be designed at the model architecture level. Furthermore, trying to cram SOTA large ML models onto edge devices even after pruning and compression is generally infeasible purely due to size: the model complexity itself must be chosen with more nuance as to more feasibly fit the device. Edge ML developers have approached this architectural challenge both through designing bespoke edge ML model architectures and through device-aware neural architecture search (NAS), which can more systematically generate feasible on-device model architectures. @@ -248,7 +248,7 @@ In this vein, a number of recent architectures have been, from inception, specif Finally, systematized pipelines for searching for performant edge-compatible model architectures are possible through frameworks like [TinyNAS](https://arxiv.org/abs/2007.10319) and [MorphNet]([https://arxiv.org/abs/1711.06798](https://arxiv.org/abs/1711.06798)). -TinyNAS is an innovative neural architecture search framework introduced in the MCUNet paper, designed to efficiently discover lightweight neural network architectures for edge devices with limited computational resources. Leveraging reinforcement learning and a compact search space of micro neural modules, TinyNAS optimizes for both accuracy and latency, enabling the deployment of deep learning models on microcontrollers, IoT devices, and other resource-constrained platforms. Specifically, TinyNAS, in conjunction with a network optimizer TinyEngine, generates different search spaces by scaling the input resolution and the model width of a model, then collects the computation FLOPs distribution of satisfying networks within the search space to evaluate its priority. TinyNAS relies on the assumption that a search space that accommodates higher FLOPs under memory constraint can produce higher accuracy models, something that the authors verified in practice in their work. In empirical performance, TinyEngine reduced models the peak memory usage by around 3.4 times and accelerated inference by 1.7 to 3.3 times compared to [TFLite]([https://www.tensorflow.org/lite](https://www.tensorflow.org/lite)) and [CMSIS-NN]([https://www.keil.com/pack/doc/CMSIS/NN/html/index.html](https://www.keil.com/pack/doc/CMSIS/NN/html/index.html)).. +TinyNAS is an innovative neural architecture search framework introduced in the MCUNet paper, designed to efficiently discover lightweight neural network architectures for edge devices with limited computational resources. Leveraging reinforcement learning and a compact search space of micro neural modules, TinyNAS optimizes for both accuracy and latency, enabling the deployment of deep learning models on microcontrollers, IoT devices, and other resource-constrained platforms. Specifically, TinyNAS, in conjunction with a network optimizer TinyEngine, generates different search spaces by scaling the input resolution and the model width of a model, then collects the computation FLOPs distribution of satisfying networks within the search space to evaluate its priority. TinyNAS relies on the assumption that a search space that accommodates higher FLOPs under memory constraint can produce higher accuracy models, something that the authors verified in practice in their work. In empirical performance, TinyEngine reduced the peak memory usage of models by around 3.4 times and accelerated inference by 1.7 to 3.3 times compared to [TFLite]([https://www.tensorflow.org/lite](https://www.tensorflow.org/lite)) and [CMSIS-NN]([https://www.keil.com/pack/doc/CMSIS/NN/html/index.html](https://www.keil.com/pack/doc/CMSIS/NN/html/index.html)).. Similarly, MorphNet is a neural network optimization framework designed to automatically reshape and morph the architecture of deep neural networks, optimizing them for specific deployment requirements. It achieves this through two steps: first, it leverages a set of customizable network morphing operations, such as widening or deepening layers, to dynamically adjust the network's structure. These operations enable the network to adapt to various computational constraints, including model size, latency, and accuracy targets, which are extremely prevalent in edge computing usage. In the second step, MorphNet uses a reinforcement learning-based approach to search for the optimal permutation of morphing operations, effectively balancing the trade-off between model size and performance. This innovative method allows deep learning practitioners to automatically tailor neural network architectures to specific application and hardware requirements, ensuring efficient and effective deployment across various platforms. @@ -273,7 +273,7 @@ In this segment, we'll delve into how practitioners can harness the principles o Numerical data, the bedrock upon which machine learning models stand, manifest in two primary forms. These are integers and floating point numbers. -**Integers** : Whole numbers, devoid of fractional components, integers (e.g., -3, 0, 42) are key in scenarios demanding discrete values. For instance, in ML, class labels in a classification task might be represented as integers, where "cat", "dog", and "bird" could be encoded as 0, 1, and 2 respectively. +**Integers:** Whole numbers, devoid of fractional components, integers (e.g., -3, 0, 42) are key in scenarios demanding discrete values. For instance, in ML, class labels in a classification task might be represented as integers, where "cat", "dog", and "bird" could be encoded as 0, 1, and 2 respectively. **Floating-Point Numbers:** Encompassing real numbers, floating-point numbers (e.g., -3.14, 0.01, 2.71828) afford the representation of values with fractional components. In ML model parameters, weights might be initialized with small floating-point values, such as 0.001 or -0.045, to commence the training process. Currently, there are 4 popular precision formats discussed below. diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 94f19404..00000000 --- a/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2018 +0,0 @@ -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: inline-block; - font-family: bootstrap-icons !important; - font-style: normal; - font-weight: normal !important; - font-variant: normal; - text-transform: none; - line-height: 1; - vertical-align: -.125em; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -.bi-123::before { content: "\f67f"; } -.bi-alarm-fill::before { content: "\f101"; } -.bi-alarm::before { content: "\f102"; } -.bi-align-bottom::before { content: "\f103"; } 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document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

      ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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a/site_libs/quarto-html/quarto-syntax-highlighting.css +++ /dev/null @@ -1,203 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -pre > code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-style: inherit; -} - -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -code span.er { - color: 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span.cn { - color: #8f5902; - font-style: inherit; -} - -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -code span.dv { - color: #AD0000; - font-style: inherit; -} - -code span.kw { - color: #003B4F; - font-style: inherit; -} - -.prevent-inlining { - content: " { - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > * " - ); - - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - console.log({ - childtop: marginChild.getBoundingClientRect().top, - scroll: window.scrollY, - top, - lastBottom, - }); - if (top < lastBottom) { - const margin = lastBottom - top; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - - console.log({ - top, - height: marginChild.getBoundingClientRect().height, - marginTop, - total: top + marginChild.getBoundingClientRect().height + marginTop, - }); - lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; - } - } -}; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Recompute the position of margin elements anytime the body size changes - if (window.ResizeObserver) { - const resizeObserver = new window.ResizeObserver( - throttle(layoutMarginEls, 50) - ); - resizeObserver.observe(window.document.body); - } - - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); - const sidebarEl = window.document.getElementById("quarto-sidebar"); - const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); - const marginSidebarEl = window.document.getElementById( - "quarto-margin-sidebar" - ); - // function to determine whether the element has a previous sibling that is active - const prevSiblingIsActiveLink = (el) => { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) - function fireSlideEnter(e) { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) - document.addEventListener("tabby", fireSlideEnter, false); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const 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- currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - - if (tocEl) { - walk(tocEl, 0); - updateActiveLink(); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync = setupSelectorSync(); - for (const [group, selectedName] of 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${topOffset}px; - margin: -${topOffset}px 0 0; - }`) - ); - } - if (init) { - window.dispatchEvent(headroomChanged); - } - init = true; - } - - // initialize headroom - var header = window.document.querySelector("#quarto-header"); - if (header && window.Headroom) { - const headroom = new window.Headroom(header, { - tolerance: 5, - onPin: function () { - const sidebars = window.document.querySelectorAll( - ".sidebar, .headroom-target" - ); - sidebars.forEach((sidebar) => { - sidebar.classList.remove("sidebar-unpinned"); - }); - updateDocumentOffset(); - }, - onUnpin: function () { - const sidebars = window.document.querySelectorAll( - ".sidebar, .headroom-target" - ); - sidebars.forEach((sidebar) => { - sidebar.classList.add("sidebar-unpinned"); - }); - updateDocumentOffset(); - }, - }); - headroom.init(); - - let frozen = false; - window.quartoToggleHeadroom = function () { - if (frozen) { - headroom.unfreeze(); - frozen = false; - } else { - headroom.freeze(); - frozen = true; - } - 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a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index f5d852d1..00000000 --- a/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1140 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query !== null && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query !== null && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - - // Remove the labeleledby attribute since it is pointing - // to a non-existent label - if (quartoSearchOptions.type === "overlay") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - // If the main document scrolls dismiss the search results - // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = () => { - setIsOpen(false); - }; - - if (showSearchResults) { - setIsOpen(true); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.map((event) => { - const maxEvents = event.objectIDs.slice(0, 20); - return { - ...event, - objectIDs: maxEvents, - }; - }); - - insights.viewedObjectIDs(...events); - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* Search Index Handling */ -// create the index -var fuseIndex = undefined; -async function readSearchData() { - // Initialize the search index on demand - if (fuseIndex === undefined) { - // create fuse index - const options = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, - }; - const fuse = new window.Fuse([], options); - - // fetch the main search.json - const response = await fetch(offsetURL("search.json")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -function createDocumentCard(createElement, icon, title, section, text, href) { - const iconEl = createElement("i", { - class: `bi bi-${icon} search-result-icon`, - }); - const titleEl = createElement("p", { class: "search-result-title" }, title); - const titleContainerEl = createElement( - "div", - { class: "search-result-title-container" }, - [iconEl, titleEl] - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["href", "section", "title", "text"].forEach((keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; - } - }); - newItem.text = highlightMatch(query, newItem.text); - return newItem; - }); - }); - return remappedHits; - } - }, - }); -} - -function fuseSearch(query, fuse, fuseOptions) { - return fuse.search(query, fuseOptions).map((result) => { - const addParam = (url, name, value) => { - const anchorParts = url.split("#"); - const baseUrl = anchorParts[0]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - section: result.item.section, - href: addParam(result.item.href, kQueryArg, query), - text: highlightMatch(query, result.item.text), - }; - }); -} From 4999961abfcd0e00a8a80554ea1fe1a1c13dcdfb Mon Sep 17 00:00:00 2001 From: Jayson Lin Date: Mon, 30 Oct 2023 18:17:30 -0400 Subject: [PATCH 03/12] fixed all text, equation, heading related issues for numerics section --- optimizations.qmd | 73 +++++++++++++++++++++++------------------------ 1 file changed, 36 insertions(+), 37 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index 6f815a59..e9b5705c 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -259,7 +259,7 @@ TinyNAS and MorphNet represent a few of the many significant advancements in the Numerics representation involves a myriad of considerations, including but not limited to, the precision of numbers, their encoding formats, and the arithmetic operations facilitated. It invariably involves a rich array of different trade-offs, where practitioners are tasked with navigating between numerical accuracy and computational efficiency. For instance, while lower-precision numerics may offer the allure of reduced memory usage and expedited computations, they concurrently present challenges pertaining to numerical stability and potential degradation of model accuracy. -### Motivation +#### Motivation The imperative for efficient numerics representation arises, particularly as efficient model optimization alone falls short when adapting models for deployment on low-powered edge devices operating under stringent constraints. @@ -295,20 +295,13 @@ Precision, delineating the exactness with which a number is represented, bifurca **Integer:** Integer representations are made using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: either +1 or -1. -| Precision | Pros | Cons | - -|------------|-----------------------------------------------------------|--------------------------------------------------| - +| **Precision** | **Pros** | **Cons** | +|------------|--------------------------------------------------|--------------------------------------------------| | **FP32** (Floating Point 32-bit) | - Standard precision used in most deep learning frameworks.\ - High accuracy due to ample representational capacity.\ - Well-suited for training. | - High memory usage.\ - Slower inference times compared to quantized models.\ - Higher energy consumption. | - | **FP16** (Floating Point 16-bit) | - Reduces memory usage compared to FP32.\ - Speeds up computations on hardware that supports FP16.\ - Often used in mixed-precision training to balance speed and accuracy. | - Lower representational capacity compared to FP32.\ - Risk of numerical instability in some models or layers. | - | **INT8** (8-bit Integer) | - Significantly reduced memory footprint compared to floating-point representations.\ - Faster inference if hardware supports INT8 computations.\ - Suitable for many post-training quantization scenarios. | - Quantization can lead to some accuracy loss.\ - Requires careful calibration during quantization to minimize accuracy degradation. | - | **INT4** (4-bit Integer) | - Even lower memory usage than INT8.\ - Further speed-up potential for inference. | - Higher risk of accuracy loss compared to INT8.\ - Calibration during quantization becomes more critical. | - | **Binary** | - Minimal memory footprint (only 1 bit per parameter).\ - Extremely fast inference due to bitwise operations.\ - Power efficient. | - Significant accuracy drop for many tasks.\ - Complex training dynamics due to extreme quantization. | - | **Ternary** | - Low memory usage but slightly more than binary.\ - Offers a middle ground between representation and efficiency. | - Accuracy might still be lower than higher precision models.\ - Training dynamics can be complex. | #### Numeric Encoding and Storage @@ -368,7 +361,7 @@ Source: [https://ieeexplore.ieee.org/document/6757323](https://ieeexplore.ieee.o Ensuring compatibility and optimized performance across diverse hardware platforms is another challenge in numerics representation. Different hardware, such as CPUs, GPUs, TPUs, and FPGAs, have varying capabilities and optimizations for handling different numeric precisions. For example, certain GPUs might be optimized for Float32 computations, while others might provide accelerations for Float16. Developing and optimizing ML models that can leverage the specific numerical capabilities of different hardware, while ensuring that the model maintains its accuracy and robustness, requires careful consideration and potentially additional development and testing efforts. -Precision and Accuracy Trade-offs +#### Precision and Accuracy Trade-offs The trade-off between numerical precision and model accuracy is a nuanced challenge in numerics representation. Utilizing lower-precision numerics, such as Float16, might conserve memory and expedite computations but can also introduce issues like quantization error and reduced numerical range. For instance, training a model with Float16 might introduce challenges in representing very small gradient values, potentially impacting the convergence and stability of the training process. Furthermore, in certain applications, such as scientific simulations or financial computations, where high precision is paramount, the use of lower-precision numerics might not be permissible due to the risk of accruing significant errors. @@ -415,13 +408,13 @@ These examples illustrate diverse scenarios where the challenges of numerics rep ### Quantization {#sec-quant} -Quantization is prevalent in various scientific and technological domains, essentially involves the **mapping or constraining of a continuous set or range into a discrete counterpart to minimize the number of bits required**. +Quantization is prevalent in various scientific and technological domains, and it essentially involves the **mapping or constraining of a continuous set or range into a discrete counterpart to minimize the number of bits required**. #### History Historically, the idea of quantization is not novel and can be traced back to ancient times, particularly in the realm of music and astronomy. In music, the Greeks utilized a system of tetrachords, segmenting the continuous range of pitches into discrete notes, thereby quantizing musical sounds. In astronomy and physics, the concept of quantization was present in the discretized models of planetary orbits, as seen in the Ptolemaic and Copernican systems. -During the 1800s, quantization-based discretization was used to approximate the calculation of integrals, and further used to investigate the impact of rounding errors on the integration result. However, the term "quantization" was firmly embedded in scientific literature with the advent of quantum mechanics in the early 20th century, where it was used to describe the phenomenon that certain physical properties, such as energy, exist only in discrete, quantized states. This principle was pivotal in explaining phenomena at the atomic and subatomic levels. In the digital age, quantization found its application in signal processing, where continuous signals are converted into a discrete digital form, and in numerical algorithms, where computations on real-valued numbers are performed with finite-precision arithmetic. +During the 1800s, quantization-based discretization was used to approximate the calculation of integrals, and further used to investigate the impact of rounding errors on the integration result. With algorithms, Lloyd's K-Means Algorithm is a classic example of quantization. However, the term "quantization" was firmly embedded in scientific literature with the advent of quantum mechanics in the early 20th century, where it was used to describe the phenomenon that certain physical properties, such as energy, exist only in discrete, quantized states. This principle was pivotal in explaining phenomena at the atomic and subatomic levels. In the digital age, quantization found its application in signal processing, where continuous signals are converted into a discrete digital form, and in numerical algorithms, where computations on real-valued numbers are performed with finite-precision arithmetic. Extending upon this second application and relevant to this section, it is used in computer science to optimize neural networks by reducing the precision of the network weights. Thus, quantization, as a concept, has been subtly woven into the tapestry of scientific and technological development, evolving and adapting to the needs and discoveries of various epochs. @@ -433,13 +426,13 @@ In signal processing, the continuous sine wave can be quantized into discrete va ![Sine Wave](images/efficientnumerics_sinewave.png) -In the quantized version shown below, the continuous sine wave is sampled at regular intervals (in this case, every \(\frac{\pi}{4}\) radians), and only these sampled values are represented in the digital version of the signal. The step-wise lines between the points show one way to represent the quantized signal in a piecewise-constant form. This is a simplified example of how analog-to-digital conversion works, where a continuous signal is mapped to a discrete set of values, enabling it to be represented and processed digitally. +In the quantized version shown below, the continuous sine wave is sampled at regular intervals (in this case, every $\frac{\pi}{4}$ radians), and only these sampled values are represented in the digital version of the signal. The step-wise lines between the points show one way to represent the quantized signal in a piecewise-constant form. This is a simplified example of how analog-to-digital conversion works, where a continuous signal is mapped to a discrete set of values, enabling it to be represented and processed digitally. ![Quantized Sine Wave](images/efficientnumerics_quantizedsine.png) -Returning to the context of Machine Learning (ML), quantization refers to the process of constraining the possible values that numerical parameters (such as weights and biases) can take to a discrete set, thereby reducing the precision of the parameters and consequently, the model's memory footprint. When properly implemented, quantization can reduce model size by up to 4x and improve inference latency and throughput by up to 2-3x. For example, an Image Classification model like ResNet-50 can be compressed from 96MB down to 24MB with 8-bit quantization.There is typically less than 1% loss in model accuracy from well tuned quantization. Accuracy can often be recovered by re-training the quantized model with quantization aware training techniques. Therefore, this technique has emerged to be very important in deploying ML models to resource-constrained environments, such as mobile devices, IoT devices, and edge computing platforms, where computational resources (memory and processing power) are limited. +Returning to the context of Machine Learning (ML), quantization refers to the process of constraining the possible values that numerical parameters (such as weights and biases) can take to a discrete set, thereby reducing the precision of the parameters and consequently, the model's memory footprint. When properly implemented, quantization can reduce model size by up to 4x and improve inference latency and throughput by up to 2-3x. For example, an Image Classification model like ResNet-50 can be compressed from 96MB down to 24MB with 8-bit quantization. There is typically less than 1% loss in model accuracy from well tuned quantization. Accuracy can often be recovered by re-training the quantized model with quantization aware training techniques. Therefore, this technique has emerged to be very important in deploying ML models to resource-constrained environments, such as mobile devices, IoT devices, and edge computing platforms, where computational resources (memory and processing power) are limited. -![](images/efficientnumerics_int8vsfloat.png) +![](images/efficientnumerics_reducedmodelsize.png) [Quantization figure - Example figure showing reduced model size from quantization]() @@ -453,15 +446,19 @@ Uniform quantization involves mapping continuous or high-precision values to a l The process for implementing uniform quantization starts with choosing a range of real numbers to be quantized. The next step is to select a quantization function and map the real values to the integers representable by the bit-width of the quantized representation. For instance, a popular choice for a quantization function is: +$$ Q(r)=Int(r/S) - Z +$$ where Q is the quantization operator, r is a real valued input (in our case, an activation or weight), S is a real valued scaling factor, and Z is an integer zero point. The Int function maps a real value to an integer value through a rounding operation. Through this function, we have effectively mapped real values r to some integer values, resulting in quantized levels which are uniformly spaced. When the need arises for practitioners to retrieve the original higher precision values, real values r can be recovered from quantized values through an operation known as **dequantization**. In the example above, this would mean performing the following operation on our quantized value: -r ̃ = S(Q(r) + Z) (~ should be on top, ignore) +$$ +\bar{r} = S(Q(r) + Z) +$$ -As discussed, some precision in the real value is lost by quantization. In this case, the recovered value r ̃ will not exactly match r due to the rounding operation. This is an important tradeoff to note; however, in many successful uses of quantization, the loss of precision can be negligible and the test accuracy remains high. Despite this, uniform quantization continues to be the current de-facto choice due to its simplicity and efficient mapping to hardware. +As discussed, some precision in the real value is lost by quantization. In this case, the recovered value $\bar{r}$ will not exactly match r due to the rounding operation. This is an important tradeoff to note; however, in many successful uses of quantization, the loss of precision can be negligible and the test accuracy remains high. Despite this, uniform quantization continues to be the current de-facto choice due to its simplicity and efficient mapping to hardware. #### Non-uniform Quantization @@ -469,7 +466,9 @@ Non-uniform quantization, on the other hand, does not maintain a consistent inte Typically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, recent work formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer Q are adjusted to minimize the difference between the original tensor and quantized counterpart. -\min\_Q ||Q(r)-r||^2 +$$ +\min_Q ||Q(r)-r||^2 +$$ Furthermore, learnable quantizers can be jointly trained with model parameters, and the quantization steps/levels are generally trained with iterative optimization or gradient descent. Additionally, clustering has been used to alleviate information loss from quantization. While capable of capturing higher levels of detail, non-uniform quantization schemes can be difficult to deploy efficiently on general computation hardware, making it less-preferred to methods which use uniform quantization. @@ -511,7 +510,7 @@ Importantly, the quality of calibration can make a difference between a quantize Symmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [\alpha, \beta] where \alpha = -\beta. For example, one symmetrical range would be based on the min/max values of the real values such that: -\alpha = \beta = max(abs(r\_max), abs(r\_min)). -Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the zeroing out of the zero point can lead to reduction in computational cost during inference ["Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation" (2023)]([https://arxiv.org/abs/2004.09602](https://arxiv.org/abs/2004.09602)) . +Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called "zeroing out of the zero point") can lead to reduction in computational cost during inference ["Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation" (2023)]([https://arxiv.org/abs/2004.09602](https://arxiv.org/abs/2004.09602)) . #### Asymmetric Quantization @@ -521,21 +520,21 @@ Asymmetric quantization maps real values to an asymmetrical clipping range that _Illustration of symmetric quantization (left) and asymmetric quantization (right). Symmetric quantization maps real values to [-127, 127], and asymmetric maps to [-128, 127]. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ -### Granularity +#### Granularity Upon deciding the type of clipping range, it is essential to tighten the range to allow a model to retain as much of its accuracy as possible. We'll be taking a look at convolutional neural networks as our way of exploring methods that fine tune the granularity of clipping ranges for quantization. The input activation of a layer in our CNN undergoes convolution with multiple convolutional filters. Every convolutional filter can possess a unique range of values. Consequently, one distinguishing feature of quantization approaches is the precision with which the clipping range [α,β] is determined for the weights. ![](images/efficientnumerics_granularity.png) _Illustration of the main forms of quantization granularities. In layerwise quantization, the same clipping range is applied to all filters which belong to the same layer. Notice how this can result in lower quantization resolutions for channels with narrow distributions, e.g. Filter 1, Filter 2, and Filter C. A higher quantization resolution can be achieved using channelwise quantization which dedicates different clipping ranges to different channels. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ -1. Layerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It's the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range. . +1. Layerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It's the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range. 2. Groupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT [Q-BERT: Hessian based ultra low precision quantization of bert] for quantizing Transformer [​​Attention Is All You Need] models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors. 3. Channelwise Quantization: This popular method uses a fixed range for each convolutional filter that is independent of other channels. Because each channel is assigned a dedicated scaling factor, this method ensures a higher quantization resolution and often results in higher accuracy. 4. Sub-channelwise Quantization: Taking channelwise quantization to the extreme, this method determines the clipping range with respect to any groups of parameters in a convolution or fully-connected layer. It may result in considerable overhead since different scaling factors need to be taken into account when processing a single convolution or fully-connected layer. Of these, channelwise quantization is the current standard used for quantizing convolutional kernels, since it enables the adjustment of clipping ranges for each individual kernel with negligible overhead. -### Static and Dynamic Quantization +#### Static and Dynamic Quantization After determining the type and granularity of the clipping range, practitioners must decide when ranges are determined in their range calibration algorithms. There are two approaches to quantizing activations: static quantization and dynamic quantization. @@ -549,7 +548,7 @@ Between the two, calculating the range dynamically usually is very costly, so mo The two prevailing techniques for quantizing models are Post Training Quantization and Quantization Aware Training. -**Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained.The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). +**Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). ![](images/efficientnumerics_lecturenote.png) ![](images/efficientnumerics_PTQ.png) @@ -573,18 +572,18 @@ Src: integer quantization for deep learning Inference: principles and empirical | **Feature/Technique** | **Post Training Quantization** | **Quantization Aware Training** | **Dynamic Quantization** | |------------------------------|------------------------------|------------------------------|------------------------------| | **Pros** | | | | -| **Simplicity** | ✓ | ✗ | ✗ | -| **Accuracy Preservation** | ✗ | ✓ | ✓ | -| **Adaptability** | ✗ | ✗ | ✓ | -| **Optimized Performance** | ✗ | ✓ | Potentially | +| Simplicity | ✓ | ✗ | ✗ | +| Accuracy Preservation | ✗ | ✓ | ✓ | +| Adaptability | ✗ | ✗ | ✓ | +| Optimized Performance | ✗ | ✓ | Potentially | | **Cons** | | | | -| **Accuracy Degradation**| ✓ | ✗ | Potentially | -| **Computational Overhead** | ✗ | ✓ | ✓ | -| **Implementation Complexity** | ✗ | ✓ | ✓ | +| Accuracy Degradation| ✓ | ✗ | Potentially | +| Computational Overhead | ✗ | ✓ | ✓ | +| Implementation Complexity | ✗ | ✓ | ✓ | | **Tradeoffs** | | | | -| **Speed vs. Accuracy** |✓ | ✗ | ✗ | -| **Accuracy vs. Cost** | ✗ | ✓ | ✗ | -| **Adaptability vs. Overhead** | ✗ | ✗ | ✓ | +| Speed vs. Accuracy |✓ | ✗ | ✗ | +| Accuracy vs. Cost | ✗ | ✓ | ✗ | +| Adaptability vs. Overhead | ✗ | ✗ | ✓ | ### Weights vs. Activations @@ -630,9 +629,9 @@ Src: [https://arxiv.org/abs/1510.00149](https://arxiv.org/abs/1510.00149) ### Edge-aware Quantization -Quantization not only reduces model size but also enables faster computations and draws less power, making it vital to edge development. Edge devices typically have tight resource constraints with compute, memory, and power, which are impossible to meet for many of the deep NN models of today. Furthermore, edge processors do not support floating point operations, making integer quantization particularly important for chips like GAP-8, a RISC-=V SoC for edge inference with a dedicated CNN accelerator, which only support integer arithmetic.. +Quantization not only reduces model size but also enables faster computations and draws less power, making it vital to edge development. Edge devices typically have tight resource constraints with compute, memory, and power, which are impossible to meet for many of the deep NN models of today. Furthermore, edge processors do not support floating point operations, making integer quantization particularly important for chips like GAP-8, a RISC-V SoC for edge inference with a dedicated CNN accelerator, which only support integer arithmetic.. -One hardware platform utilizing quantization is the ARM Cortex-M group of 32=bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google's emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Recently, there has been significant strides in the computing power of edge processors, enabling the deployment and inference of costly NN models previously limited to servers. +One hardware platform utilizing quantization is the ARM Cortex-M group of 32-bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google's emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Recently, there has been significant strides in the computing power of edge processors, enabling the deployment and inference of costly NN models previously limited to servers. ![](images/efficientnumerics_edgequant.png) @@ -863,6 +862,6 @@ In this chapter we've discussed model optimization across the software-hardware We then explored efficient numerics representations, where we covered the basics of numerics, numeric encodings and storage, benefits of efficient numerics, and the nuances of numeric representation with memory usage, computational complexity, hardware compatibility, and tradeoff scenarios. We finished by honing in on an efficient numerics staple: quantization, where we examined its history, calibration, techniques, and interaction with pruning. -Finally, we looked at how we can make optimizations specific to the hardware we have. We explored how we can find model architectures tailored to the hardware, make optimizations in the kernel to better handle the model, and frameworks built to make the most use out of the hardware. We also looked at how we can go the other way around and build hardware around our specific software and talked about splitting networks to run on multiple processor available on the edge device. +Finally, we looked at how we can make optimizations specific to the hardware we have. We explored how we can find model architectures tailored to the hardware, make optimizations in the kernel to better handle the model, and frameworks built to make the most use out of the hardware. We also looked at how we can go the other way around and build hardware around our specific software and talked about splitting networks to run on multiple processors available on the edge device. By understanding the full picture of the degrees of freedom within model optimization both away and close to the hardware and the tradeoffs to consider when implementing these methods, practitioners can develop a more thoughtful pipeline for compressing their workloads onto edge devices. From 14e42f11ec3c43dc08b835e830516c7570f28b33 Mon Sep 17 00:00:00 2001 From: Jayson Lin Date: Mon, 30 Oct 2023 21:17:02 -0400 Subject: [PATCH 04/12] sources added - numerics section --- optimizations.qmd | 78 ++++++++++++++++------------------------------- references.bib | 53 ++++++++++++++++++++++++++++++++ 2 files changed, 80 insertions(+), 51 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index e9b5705c..b95a36f1 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -291,7 +291,7 @@ Precision, delineating the exactness with which a number is represented, bifurca **Bfloat16:** Brain Floating-Point Format or Bfloat16, also employs 16 bits but allocates them differently compared to FP16: 1 bit for the sign, 8 bits for the exponent, and 7 bits for the fraction. This format, developed by Google, prioritizes a larger exponent range over precision, making it particularly useful in deep learning applications where the dynamic range is crucial. -![Three floating-point formats. Source: [Google blog](google.com)](https://storage.googleapis.com/gweb-cloudblog-publish/images/Three_floating-point_formats.max-624x261.png) +![Three floating-point formats (@threefloat).](https://storage.googleapis.com/gweb-cloudblog-publish/images/Three_floating-point_formats.max-624x261.png) **Integer:** Integer representations are made using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: either +1 or -1. @@ -351,10 +351,11 @@ Numerical precision directly impacts computational complexity, influencing the t In addition to pure runtimes, there is also a concern over energy efficiency. Not all numerical computations are created equal from the underlying hardware standpoint. Some numerical operations are more energy efficient than others. For example, the figure below shows that integer addition is much more energy efficient than integer multiplication. -![](images/efficientnumerics_100x.png) -![](images/efficientnumerics_horowitz.png) -Source: [https://ieeexplore.ieee.org/document/6757323](https://ieeexplore.ieee.org/document/6757323) -![](images/efficientnumerics_int8vsfloat.png) +![In this visualization, we can note the massive reductions in energy use by quantized operations (@energyproblem).](images/efficientnumerics_100x.png) + +![Tables comparing energy use by quantized operations (@energyproblem).](images/efficientnumerics_horowitz.png) + +![Graph showing the speed differences for three different models in normal and quantized form.](images/efficientnumerics_int8vsfloat.png) #### Hardware Compatibility @@ -432,9 +433,7 @@ In the quantized version shown below, the continuous sine wave is sampled at reg Returning to the context of Machine Learning (ML), quantization refers to the process of constraining the possible values that numerical parameters (such as weights and biases) can take to a discrete set, thereby reducing the precision of the parameters and consequently, the model's memory footprint. When properly implemented, quantization can reduce model size by up to 4x and improve inference latency and throughput by up to 2-3x. For example, an Image Classification model like ResNet-50 can be compressed from 96MB down to 24MB with 8-bit quantization. There is typically less than 1% loss in model accuracy from well tuned quantization. Accuracy can often be recovered by re-training the quantized model with quantization aware training techniques. Therefore, this technique has emerged to be very important in deploying ML models to resource-constrained environments, such as mobile devices, IoT devices, and edge computing platforms, where computational resources (memory and processing power) are limited. -![](images/efficientnumerics_reducedmodelsize.png) - -[Quantization figure - Example figure showing reduced model size from quantization]() +![Figure comparing the sizes of three models with their quantized forms](images/efficientnumerics_reducedmodelsize.png) There are several dimensions to quantization such as uniformity, stochasticity (or determinism), symmetry, granularity (across layers/channels/groups or even within channels), range calibration considerations (static vs dynamic), and fine-tuning methods (QAT, PTQ, ZSQ). We examine these below. @@ -464,7 +463,7 @@ As discussed, some precision in the real value is lost by quantization. In this Non-uniform quantization, on the other hand, does not maintain a consistent interval between quantized values. This approach might be used to allocate more possible discrete values in regions where the parameter values are more densely populated, thereby preserving more detail where it is most needed. For instance, in bell-shaped distributions of weights with long tails, a set of weights in a model predominantly lies within a certain range; thus, more quantization levels might be allocated to that range to preserve finer details, enabling us to better capture information. However, one major weakness of non-uniform quantization is that it requires dequantization before higher precision computations due to its non-uniformity, restricting its ability to accelerate computation compared to uniform quantization. -Typically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, recent work formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer Q are adjusted to minimize the difference between the original tensor and quantized counterpart. +Typically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, [recent work](https://arxiv.org/abs/1802.00150) formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer Q are adjusted to minimize the difference between the original tensor and quantized counterpart. $$ \min_Q ||Q(r)-r||^2 @@ -472,9 +471,7 @@ $$ Furthermore, learnable quantizers can be jointly trained with model parameters, and the quantization steps/levels are generally trained with iterative optimization or gradient descent. Additionally, clustering has been used to alleviate information loss from quantization. While capable of capturing higher levels of detail, non-uniform quantization schemes can be difficult to deploy efficiently on general computation hardware, making it less-preferred to methods which use uniform quantization. -![](images/efficientnumerics_uniformnonuniform.png) - -_Comparison between uniform quantization (left) and non-uniform quantization (right) (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ +![Comparison between uniform quantization (left) and non-uniform quantization (right) (@surveyofquant).](images/efficientnumerics_uniformnonuniform.png) #### Stochastic Quantization @@ -500,9 +497,7 @@ Entropy: Use KL divergence to minimize information loss between the original flo Percentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values. -![](images/efficientnumerics_calibration.png) - -Src: Integer quantization for deep learning inference +![(@intquantfordeepinf)](images/efficientnumerics_calibration.png) Importantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it's an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric. @@ -510,22 +505,19 @@ Importantly, the quality of calibration can make a difference between a quantize Symmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [\alpha, \beta] where \alpha = -\beta. For example, one symmetrical range would be based on the min/max values of the real values such that: -\alpha = \beta = max(abs(r\_max), abs(r\_min)). -Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called "zeroing out of the zero point") can lead to reduction in computational cost during inference ["Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation" (2023)]([https://arxiv.org/abs/2004.09602](https://arxiv.org/abs/2004.09602)) . +Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called "zeroing out of the zero point") can lead to reduction in computational cost during inference ["Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation" (2023)]([https://arxiv.org/abs/2004.09602 (@intquantfordeepinf)](https://arxiv.org/abs/2004.09602)) . #### Asymmetric Quantization Asymmetric quantization maps real values to an asymmetrical clipping range that isn't necessarily centered around 0. It involves choosing a range [\alpha, \beta] where \alpha \neq -\beta. For example, selecting a range based on the minimum and maximum real values, or where \alpha = r\_min and \beta = r\_max, creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn't always zero out the real value zero. -![](images/efficientnumerics_symmetry.png) - -_Illustration of symmetric quantization (left) and asymmetric quantization (right). Symmetric quantization maps real values to [-127, 127], and asymmetric maps to [-128, 127]. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ +![Illustration of symmetric quantization (left) and asymmetric quantization (right). Symmetric quantization maps real values to [-127, 127], and asymmetric maps to [-128, 127] (@surveyofquant).](images/efficientnumerics_symmetry.png) #### Granularity Upon deciding the type of clipping range, it is essential to tighten the range to allow a model to retain as much of its accuracy as possible. We'll be taking a look at convolutional neural networks as our way of exploring methods that fine tune the granularity of clipping ranges for quantization. The input activation of a layer in our CNN undergoes convolution with multiple convolutional filters. Every convolutional filter can possess a unique range of values. Consequently, one distinguishing feature of quantization approaches is the precision with which the clipping range [α,β] is determined for the weights. -![](images/efficientnumerics_granularity.png) -_Illustration of the main forms of quantization granularities. In layerwise quantization, the same clipping range is applied to all filters which belong to the same layer. Notice how this can result in lower quantization resolutions for channels with narrow distributions, e.g. Filter 1, Filter 2, and Filter C. A higher quantization resolution can be achieved using channelwise quantization which dedicates different clipping ranges to different channels. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ +![Illustration of the main forms of quantization granularities. In layerwise quantization, the same clipping range is applied to all filters which belong to the same layer. Notice how this can result in lower quantization resolutions for channels with narrow distributions, e.g. Filter 1, Filter 2, and Filter C. A higher quantization resolution can be achieved using channelwise quantization which dedicates different clipping ranges to different channels (@surveyofquant).](images/efficientnumerics_granularity.png) 1. Layerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It's the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range. 2. Groupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT [Q-BERT: Hessian based ultra low precision quantization of bert] for quantizing Transformer [​​Attention Is All You Need] models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors. @@ -550,24 +542,17 @@ The two prevailing techniques for quantizing models are Post Training Quantizati **Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). -![](images/efficientnumerics_lecturenote.png) -![](images/efficientnumerics_PTQ.png) +![Table showing the differences between different precisions of quantization.](images/efficientnumerics_lecturenote.png) -_In PTQ, a pretrained model is calibrated using calibration data (e.g., a small subset of training data) to compute the clipping ranges and scaling factors. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )_ +![In PTQ, a pretrained model is calibrated using calibration data (e.g., a small subset of training data) to compute the clipping ranges and scaling factors (@surveyofquant).](images/efficientnumerics_PTQ.png) **Quantization Aware Training** - Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization Aware Training (QAT) [Quantizing deep convolutional networks for efficient inference](2018)([https://arxiv.org/abs/1806.08342](https://arxiv.org/abs/1806.08342)).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. -![](images/efficientnumerics_QAT.png) - -_In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation. Note: the calibration process is often conducted in parallel with the finetuning process for QAT. (Credit: __**A Survey of Quantization Methods for Efficient Neural Network Inference**__ )._ +![In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation. Note: the calibration process is often conducted in parallel with the finetuning process for QAT (@surveyofquant).](images/efficientnumerics_QAT.png) -Src: Integer quantization for deep learning inference ![](images/efficientnumerics_PTQQAT.png) +![Note that QAT is an extension of PTQ. It receives the model quantized by PTQ and retrains it to finetune quantized parameters (@deci).](images/efficientnumerics_PTQQAT.png) -_Note that QAT is an extension of PTQ. It receives the model quantized by PTQ and retrains it to finetune quantized parameters._ Src: https://deci.ai/quantization-and-quantization-aware-training/ - -![](images/efficientnumerics_PTQQATsummary.png) - -Src: integer quantization for deep learning Inference: principles and empirical evaluations +![(@intquantfordeepinf)](images/efficientnumerics_PTQQATsummary.png) | **Feature/Technique** | **Post Training Quantization** | **Quantization Aware Training** | **Dynamic Quantization** | |------------------------------|------------------------------|------------------------------|------------------------------| @@ -590,42 +575,33 @@ Src: integer quantization for deep learning Inference: principles and empirical Weight Quantization: Involves converting the continuous or high-precision weights of a model to lower-precision, such as converting Float32 weights to quantized INT8 (integer) weights. This reduces the model size, thereby reducing the memory required to store the model and the computational resources needed to perform inference. For example, consider a weight matrix in a neural network layer with Float32 weights as [0.215, -1.432, 0.902, ...]. Through weight quantization, these might be mapped to INT8 values like [27, -183, 115, ...], significantly reducing the memory required to store them. -[Figure X.2 - Diagram of quantizing weights and activations] ![](images/efficientnumerics_weightsactivations.png) +![Diagram of quantizing weights and activations](images/efficientnumerics_weightsactivations.png) -Activation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, recent work has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](2023)(https://arxiv.org/pdf/2306.00978.pdf). +Activation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, [recent work (@awq)](https://arxiv.org/pdf/2306.00978.pdf) has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights. ### Trade-offs Quantization invariably introduces a trade-off between model size/performance and accuracy. While it significantly reduces the memory footprint and can accelerate inference, especially on hardware optimized for low-precision arithmetic, the reduced precision can degrade model accuracy. Model Size: A model with weights represented as Float32 being quantized to INT8 can theoretically reduce the model size by a factor of 4, enabling it to be deployed on devices with limited memory. -![](images/efficientnumerics_modelsizes.png) -Src: https://arxiv.org/abs/2211.10438 +![(@smoothquant)](images/efficientnumerics_modelsizes.png) Inference Speed: Quantization can also accelerate inference, as lower-precision arithmetic is computationally less expensive. For example, certain hardware accelerators, like Google's Edge TPU, are optimized for INT8 arithmetic and can perform inference significantly faster with INT8 quantized models compared to their floating-point counterparts. -![](images/efficientnumerics_benefitsofprecision.png) - -Src: Integer quantization for deep learning inference +![(@intquantfordeepinf)](images/efficientnumerics_benefitsofprecision.png) -Accuracy: The reduction in numerical precision post-quantization can lead to a degradation in model accuracy, which might be acceptable in certain applications (e.g., image classification) but not in others (e.g., medical diagnosis). Therefore, post-quantization, the model typically requires re-calibration or fine-tuning to mitigate accuracy loss. Furthermore, recent work has explored the use of Activation-aware Weight Quantization which is based on the observation that protecting only 1% of salient weights can greatly reduce quantization error [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](2023)(https://arxiv.org/pdf/2306.00978.pdf). +Accuracy: The reduction in numerical precision post-quantization can lead to a degradation in model accuracy, which might be acceptable in certain applications (e.g., image classification) but not in others (e.g., medical diagnosis). Therefore, post-quantization, the model typically requires re-calibration or fine-tuning to mitigate accuracy loss. Furthermore, recent work has explored the use of [Activation-aware Weight Quantization (@awq)](https://arxiv.org/pdf/2306.00978.pdf) which is based on the observation that protecting only 1% of salient weights can greatly reduce quantization error. -![](images/efficientnumerics_alexnet.png) - -Src: [https://arxiv.org/abs/1510.00149](https://arxiv.org/abs/1510.00149) - -[Figure]() +![(@deepcompress)](images/efficientnumerics_alexnet.png) ### Quantization and Pruning Pruning and quantization work well together, and it's been found that pruning doesn't hinder quantization. In fact, pruning can help reduce quantization error. Intuitively, this is due to pruning reducing the number of weights to quantize, thereby reducing the accumulated error from quantization. For example, an unpruned AlexNet has 60 million weights to quantize whereas a pruned AlexNet only has 6.7 million weights to quantize. This significant drop in weights helps reduce the error between quantizing the unpruned AlexNet vs. the pruned AlexNet. Furthermore, recent work has found that quantization-aware pruning generates more computationally efficient models than either pruning or quantization alone; It typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization [Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference][2021](https://arxiv.org/pdf/2102.11289.pdf). -![](images/efficientnumerics_qp1.png) - -Src: [https://arxiv.org/abs/1510.00149](https://arxiv.org/abs/1510.00149) +![(@deepcompress)](images/efficientnumerics_qp1.png) -![](images/efficientnumerics_qp2.png) +![(@deepcompress)](images/efficientnumerics_qp2.png) ### Edge-aware Quantization @@ -633,7 +609,7 @@ Quantization not only reduces model size but also enables faster computations an One hardware platform utilizing quantization is the ARM Cortex-M group of 32-bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google's emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Recently, there has been significant strides in the computing power of edge processors, enabling the deployment and inference of costly NN models previously limited to servers. -![](images/efficientnumerics_edgequant.png) +![(@surveyofquant)](images/efficientnumerics_edgequant.png) In addition to being an indispensable technique for many edge processors, quantization has also brought noteworthy improvements to non-edge processors such as encouraging such processors to meet the Service Level Agreement (SLA) requirements such as 99th percentile latency. diff --git a/references.bib b/references.bib index 4f1e6048..f73b5933 100644 --- a/references.bib +++ b/references.bib @@ -988,3 +988,56 @@ @inproceedings{coleman2022similarity pages={6402--6410}, year={2022} } +@misc{threefloat, + title = {Three Floating Point Formats}, + url = {https://storage.googleapis.com/gweb-cloudblog-publish/images/Three_floating-point_formats.max-624x261.png}, + urldate = {2023-10-20}, + author = {Google}, + year = {2023} +} +@misc{energyproblem, + title = {Computing's energy problem (and what we can do about it)}, + url = {https://ieeexplore.ieee.org/document/6757323}, + urldate = {2014-03-06}, + author = {ISSCC}, + year = {2014} +} +@misc{surveyofquant, + title = {A Survey of Quantization Methods for Efficient Neural Network Inference)}, + url = {https://arxiv.org/abs/2103.13630}, + urldate = {2021-06-21}, + author = {Gholami and Kim, Dong and Yao, Mahoney and Keutzer}, + year = {2021} +} +@misc{intquantfordeepinf, + title = {Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation)}, + url = {https://arxiv.org/abs/2004.09602}, + urldate = {2020-04-20}, + author = {Wu and Judd, Zhang and Isaev, Micikevicius}, + year = {2020} +} +@misc{deci, + title = {The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training}, + url = {https://deci.ai/quantization-and-quantization-aware-training/}, +} +@misc{awq, + title = {AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration}, + url = {https://arxiv.org/pdf/2306.00978.pdf}, + urldate = {2023-10-03}, + author = {Lin and Tang, Tang and Yang, Dang and Gan, Han}, + year = {2023} +} +@misc{smoothquant, + title = {SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models}, + url = {https://arxiv.org/abs/2211.10438}, + urldate = {2023-06-05}, + author = {Xiao and Lin, Seznec and Wu, Demouth and Han}, + year = {2023} +} +@misc{deepcompress, + title = {Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding}, + url = {https://arxiv.org/abs/1510.00149}, + urldate = {2016-02-15}, + author = {Han and Mao and Dally}, + year = {2016} +} \ No newline at end of file From b1abe28bce66af0f04736cf2951da5c628e5f163 Mon Sep 17 00:00:00 2001 From: Jayson Lin Date: Mon, 30 Oct 2023 21:41:43 -0400 Subject: [PATCH 05/12] Updated images to not have descriptions --- 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For example, 99% calibration would clip 1% of the largest magnitude values. -![(@intquantfordeepinf)](images/efficientnumerics_calibration.png) +![Histogram of input activatsions to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png) Importantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it's an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric. @@ -552,7 +552,7 @@ The two prevailing techniques for quantizing models are Post Training Quantizati ![Note that QAT is an extension of PTQ. It receives the model quantized by PTQ and retrains it to finetune quantized parameters (@deci).](images/efficientnumerics_PTQQAT.png) -![(@intquantfordeepinf)](images/efficientnumerics_PTQQATsummary.png) +![Summary of Post Training Quantization and Quantization Aware Training. PTQ reports the best accuracy and corresponding calibration for each model (@intquantfordeepinf).](images/efficientnumerics_PTQQATsummary.png) | **Feature/Technique** | **Post Training Quantization** | **Quantization Aware Training** | **Dynamic Quantization** | |------------------------------|------------------------------|------------------------------|------------------------------| @@ -585,23 +585,23 @@ Quantization invariably introduces a trade-off between model size/performance an Model Size: A model with weights represented as Float32 being quantized to INT8 can theoretically reduce the model size by a factor of 4, enabling it to be deployed on devices with limited memory. -![(@smoothquant)](images/efficientnumerics_modelsizes.png) +![The model size of large language models is developing at a faster pace than the GPU memory in recent years, leading to a big gap between the supply and demand for memory. Quantization and model compression techniques can help bridge the gap (@smoothquant).](images/efficientnumerics_modelsizes.png) Inference Speed: Quantization can also accelerate inference, as lower-precision arithmetic is computationally less expensive. For example, certain hardware accelerators, like Google's Edge TPU, are optimized for INT8 arithmetic and can perform inference significantly faster with INT8 quantized models compared to their floating-point counterparts. -![(@intquantfordeepinf)](images/efficientnumerics_benefitsofprecision.png) +![Benefits of lower precision data types for tensor operations on the NVIDIA Turing GPU architecture (@intquantfordeepinf).](images/efficientnumerics_benefitsofprecision.png) Accuracy: The reduction in numerical precision post-quantization can lead to a degradation in model accuracy, which might be acceptable in certain applications (e.g., image classification) but not in others (e.g., medical diagnosis). Therefore, post-quantization, the model typically requires re-calibration or fine-tuning to mitigate accuracy loss. Furthermore, recent work has explored the use of [Activation-aware Weight Quantization (@awq)](https://arxiv.org/pdf/2306.00978.pdf) which is based on the observation that protecting only 1% of salient weights can greatly reduce quantization error. -![(@deepcompress)](images/efficientnumerics_alexnet.png) +![Accuracy of AlexNet with different aggressiveness of weight sharing and quantization. 8/5 bit quantization has no loss of accuracy; 8/4 bit quantization, which is more hardware friendly, has negligible loss of accuracy of 0.01%; To be really aggressive, 4/2 bit quantization resulted in 1.99% and 2.60% loss of accuracy (@deepcompress).](images/efficientnumerics_alexnet.png) ### Quantization and Pruning Pruning and quantization work well together, and it's been found that pruning doesn't hinder quantization. In fact, pruning can help reduce quantization error. Intuitively, this is due to pruning reducing the number of weights to quantize, thereby reducing the accumulated error from quantization. For example, an unpruned AlexNet has 60 million weights to quantize whereas a pruned AlexNet only has 6.7 million weights to quantize. This significant drop in weights helps reduce the error between quantizing the unpruned AlexNet vs. the pruned AlexNet. Furthermore, recent work has found that quantization-aware pruning generates more computationally efficient models than either pruning or quantization alone; It typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization [Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference][2021](https://arxiv.org/pdf/2102.11289.pdf). -![(@deepcompress)](images/efficientnumerics_qp1.png) +![Accuracy v.s. compression rate under different compression methods. Pruning and quantization works best when combined (@deepcompress).](images/efficientnumerics_qp1.png) -![(@deepcompress)](images/efficientnumerics_qp2.png) +![Pruning doesn't hurt quantization. Dashed: quantization on unpruned network. Solid: quantization on pruned network; Accuracy begins to drop at the same number of quantization bits whether or not the network has been pruned. ALthough pruning made the number of parameters less, quantization still works well, or even better (3 bits case on the left figure) as in the unpruned network (@deepcompress).](images/efficientnumerics_qp2.png) ### Edge-aware Quantization @@ -609,7 +609,7 @@ Quantization not only reduces model size but also enables faster computations an One hardware platform utilizing quantization is the ARM Cortex-M group of 32-bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google's emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Recently, there has been significant strides in the computing power of edge processors, enabling the deployment and inference of costly NN models previously limited to servers. -![(@surveyofquant)](images/efficientnumerics_edgequant.png) +![Throughput comparison of different commerical edge processors for NN inference at the edge (@surveyofquant).](images/efficientnumerics_edgequant.png) In addition to being an indispensable technique for many edge processors, quantization has also brought noteworthy improvements to non-edge processors such as encouraging such processors to meet the Service Level Agreement (SLA) requirements such as 99th percentile latency. 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z2u#LaY~|<8KTqOEJ<8ohy8-%p1B5AbiT|4c;Kt2Nz1PSL zKq7<*@vW=F(tplqU^}Du=ODYoe7zaGQi}r6r+l$_cN`XmmNnA4_xGrwUiVw$_w$Xv z{w=DcnEQq@0wq8fU+fvTg(pEz31rTuPVUmx=x_!HN3yul7=3_iIq@4T9|9MV| z1(*fDVYf@geomTTnV2t zOYeRRc!Na;GW7SqNr|*E0*l?V^UC6n1E<2Vzxs~y`re*?##J5!L{C0CWdPTwxf_Fl zGUO=0+Ot2c(#Bc1=i^RvTVroy|G3F8x$ifcPTeU^vaPz*1K_2hqO07fX#3`W0eSi& A;Q#;t literal 0 HcmV?d00001 diff --git a/optimizations.qmd b/optimizations.qmd index 57de4013..a6598e86 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -657,9 +657,9 @@ Efficient hardware implementation transcends the selection of suitable component ### Hardware-Aware Neural Architecture Search -Focusing only on the accuracy when performing Neural Architecture Search leads to models that are exponentially complex and require increasing memory and compute. This has lead to hardware constraints limiting the exploitation of the deep learning models at their full potential. Manually designing the architecture of the model is even harder when considering the hardware variety and limitations. This has lead to the creation of Hardware-aware Neural Architecture Search that incorporate the hardware contractions into their search and optimize the search space for a specific hardware and accuracy. HW-NAS can be catogrized based how it optimizes for hardware. We will briefly explore these categories and leave links to related papers for the interested reader. +Focusing only on the accuracy when performing Neural Architecture Search leads to models that are exponentially complex and require increasing memory and compute. This has lead to hardware constraints limiting the exploitation of the deep learning models at their full potential. Manually designing the architecture of the model is even harder when considering the hardware variety and limitations. This has lead to the creation of Hardware-aware Neural Architecture Search that incorporate the hardware contractions into their search and optimize the search space for a specific hardware and accuracy. HW-NAS can be categorized based how it optimizes for hardware. We will briefly explore these categories and leave links to related papers for the interested reader. -![Taxonomy of HW-NAS [1](https://www.ijcai.org/proceedings/2021/592)](images/modeloptimization_HW-NAS.png) +![Taxonomy of HW-NAS (Benmeziane et al. ([2021](https://www.ijcai.org/proceedings/2021/592)))](images/modeloptimization_HW-NAS.png) #### Single Target, Fixed Platfrom Configuration @@ -670,19 +670,19 @@ Two approaches fall under this category ##### Hardware-aware Search Strategy -Here, the search is a multi-objective optimization problem, where both the accuracy and hardware cost guide the searching algorithm to find the most efficient architecture. [1](https://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper.html)[2](https://arxiv.org/abs/1812.00332)[3](https://arxiv.org/abs/1812.03443) +Here, the search is a multi-objective optimization problem, where both the accuracy and hardware cost guide the searching algorithm to find the most efficient architecture. (Tan et al. ([2019](https://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper.html)))(Cai et al. ([2018](https://arxiv.org/abs/1812.00332)))(Wu et al. ([2018](https://arxiv.org/abs/1812.03443))) ##### Hardware-aware Search Space -Here, the search space is restricted to the architectures that perform well on the specific hardware. This can be achieved by either measuring the operators (Conv operator, Pool operator, …) performance, or define a set of rules that limit the search space. [1](https://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.html) +Here, the search space is restricted to the architectures that perform well on the specific hardware. This can be achieved by either measuring the operators (Conv operator, Pool operator, …) performance, or define a set of rules that limit the search space. (Zhang et al. ([2020](https://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.html))) #### Single Target, Multiple Platform Configurations -Some hardwares may have different configurations. For example, FPGAs have Configurable Logic Blocks (CLBs) that can be configured by the firmware. This method allows for the HW-NAS to explore different configurations. [1](https://arxiv.org/abs/1901.11211)[2](https://arxiv.org/abs/2002.04116) +Some hardwares may have different configurations. For example, FPGAs have Configurable Logic Blocks (CLBs) that can be configured by the firmware. This method allows for the HW-NAS to explore different configurations. (Jiang et al. ([2019](https://arxiv.org/abs/1901.11211)))(Yang et al. ([2020](https://arxiv.org/abs/2002.04116))) #### Multiple Targets -This category aims at optimizing a single model for multiple hardwares. This can be helpful for mobile devices development as it can optimize to different phones models. [1](https://arxiv.org/abs/2008.08178)[2](https://ieeexplore.ieee.org/document/9102721) +This category aims at optimizing a single model for multiple hardwares. This can be helpful for mobile devices development as it can optimize to different phones models. (Chu et al. ([2020](https://arxiv.org/abs/2008.08178)))(Jiang et al. ([2020](https://ieeexplore.ieee.org/document/9102721))) #### Examples of Hardware-Aware Neural Architecture Search @@ -692,13 +692,14 @@ TinyNAS adopts a two stage approach to finding an optimal architecture for model First, TinyNAS generate multiple search spaces by varying the input resolution of the model, and the number of channels of the layers of the model. Then, TinyNAS chooses a search space based on the FLOPs (Floating Point Operations Per Second) of each search space -Then, TinyNAS performs a search operation on the chosen space to find the optimal architecture for the specific constraints of the microcontroller. [1](https://arxiv.org/abs/2007.10319) +Then, TinyNAS performs a search operation on the chosen space to find the optimal architecture for the specific constraints of the microcontroller. (Han et al. ([2020](https://arxiv.org/abs/2007.10319))) + +![A diagram showing how search spaces with high probability of finding an architecture with large number of FLOPs provide models with higher accuracy (Han et al. ([2020](https://arxiv.org/abs/2007.10319)))](images/modeloptimization_TinyNAS.png) -![A diagram showing how search spaces with high probability of finding an architecture with large number of FLOPs provide models with higher accuracy [1](https://arxiv.org/abs/2007.10319)](images/modeloptimization_TinyNAS.png) #### Topology-Aware NAS -Focuses on creating and optimizing a search space that aligns with the hardware topology of the device. [1](https://arxiv.org/pdf/1911.09251.pdf) +Focuses on creating and optimizing a search space that aligns with the hardware topology of the device. (Zhang et al. ([2019](https://arxiv.org/pdf/1911.09251.pdf))) ### Challenges of Hardware-Aware Neural Architecture Search @@ -726,51 +727,52 @@ Similarly to blocking, tiling divides data and computation into chunks, but exte ##### Optimized Kernel Libraries -This comprises developing optimized kernels that take full advantage of a specific hardware. One example is the CMSIS-NN library, which is a collection of efficient neural network kernels developed to optimize the performance and minimize the memory footprint of models on Arm Cortex-M processors, which are common on IoT edge devices. The kernel leverage multiple hardware capabilities of Cortex-M processors like Single Instruction Multple Data (SIMD), Floating Point Units (FPUs) and M-Profile Vector Extensions (MVE). These optimization make common operations like matrix multiplications more efficient, boosting the performance of model operations on Cortex-M processors. [1](https://arxiv.org/abs/1801.06601#:~:text=This%20paper%20presents%20CMSIS,for%20intelligent%20IoT%20edge%20devices) +This comprises developing optimized kernels that take full advantage of a specific hardware. One example is the CMSIS-NN library, which is a collection of efficient neural network kernels developed to optimize the performance and minimize the memory footprint of models on Arm Cortex-M processors, which are common on IoT edge devices. The kernel leverage multiple hardware capabilities of Cortex-M processors like Single Instruction Multple Data (SIMD), Floating Point Units (FPUs) and M-Profile Vector Extensions (MVE). These optimization make common operations like matrix multiplications more efficient, boosting the performance of model operations on Cortex-M processors. (Lai et al. ([2018](https://arxiv.org/abs/1801.06601#:~:text=This%20paper%20presents%20CMSIS,for%20intelligent%20IoT%20edge%20devices))) ### Compute-in-Memory (CiM) -This is one example of Algorithm-Hardware Co-design. CiM is a computing paradigm that performs computation within memory. Therefore, CiM architectures allow for operations to be performed directly on the stored data, without the need to shuttle data back and forth between separate processing and memory units. This design paradigm is particularly beneficial in scenarios where data movement is a primary source of energy consumption and latency, such as in TinyML applications on edge devices. Through algorithm-hardware co-design, the algorithms can be optimized to leverage the unique characteristics of CiM architectures, and conversely, the CiM hardware can be customized or configured to better support the computational requirements and characteristics of the algorithms. This is achieved by using the analog properties of memory cells, such as addition and multiplication in DRAM. [1](https://arxiv.org/abs/2111.06503) +This is one example of Algorithm-Hardware Co-design. CiM is a computing paradigm that performs computation within memory. Therefore, CiM architectures allow for operations to be performed directly on the stored data, without the need to shuttle data back and forth between separate processing and memory units. This design paradigm is particularly beneficial in scenarios where data movement is a primary source of energy consumption and latency, such as in TinyML applications on edge devices. Through algorithm-hardware co-design, the algorithms can be optimized to leverage the unique characteristics of CiM architectures, and conversely, the CiM hardware can be customized or configured to better support the computational requirements and characteristics of the algorithms. This is achieved by using the analog properties of memory cells, such as addition and multiplication in DRAM. (Zhou et al. ([2021](https://arxiv.org/abs/2111.06503))) ![A figure showing how Computing in Memory can be used for always-on tasks to offload tasks of the power consuming processing unit [1](https://arxiv.org/abs/2111.06503)](images/modeloptimization_CiM.png) -## Memory Access Optimization +### Memory Access Optimization Different devices may have different memory hierarchies. Optimizing for the specific memory hierarchy in the specific hardware can lead to great performance improvements by reducing the costly operations of reading and writing to memory. Dataflow optimization can be achieved by optimizing for reusing data within a single layer and across multiple layers. This dataflow optimization can be tailored to the specific memory hierarchy of the hardware, which can lead to greater benefits than general optimizations for different hardwares. ### Leveraging Sparsity -Pruning is a fundamental approach to compress models to make them compatible with resource constrained devices. This results in sparse models where a lot of weights are 0's. Therefore, leveraging this sparsity can lead to significant improvements in performance. Tools were created to achieve exactly this. RAMAN, is a sparseTinyML accelerator designed for inference on edge devices. RAMAN overlap input and output activations on the same memory space, reducing storage requirements by up to 50%. [1](https://ar5iv.labs.arxiv.org/html/2306.06493) +Pruning is a fundamental approach to compress models to make them compatible with resource constrained devices. This results in sparse models where a lot of weights are 0's. Therefore, leveraging this sparsity can lead to significant improvements in performance. Tools were created to achieve exactly this. RAMAN, is a sparseTinyML accelerator designed for inference on edge devices. RAMAN overlap input and output activations on the same memory space, reducing storage requirements by up to 50%. (Krishna et al. ([2023](https://ar5iv.labs.arxiv.org/html/2306.06493))) + +![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations (Kung et al. ([2018](https://arxiv.org/abs/1811.04770)))](images/modeloptimization_sparsity.png) -![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations[1](https://arxiv.org/abs/1811.04770)](images/modeloptimization_sparsity.png) ### Optimization Frameworks Optimization Frameworks have been introduced to exploit the specific capabilities of the hardware to accelerate the software. One example of such a framework is hls4ml. This open-source software-hardware co-design workflow aids in interpreting and translating machine learning algorithms for implementation with both FPGA and ASIC technologies, enhancing their. Features such as network optimization, new Python APIs, quantization-aware pruning, and end-to-end FPGA workflows are embedded into the hls4ml framework, leveraging parallel processing units, memory hierarchies, and specialized instruction sets to optimize models for edge hardware. Moreover, hls4ml is capable of translating machine learning algorithms directly into FPGA firmware. -![A Diagram showing the workflow with the hls4ml framework [1](https://arxiv.org/pdf/2103.05579.pdf)](images/modeloptimization_hls4ml.png) +![A Diagram showing the workflow with the hls4ml framework (Fahim et al. ([2021](https://arxiv.org/pdf/2103.05579.pdf)))](images/modeloptimization_hls4ml.png) -One other framework for FPGAs that focuses on a holistic approach is CFU Playground [1](https://arxiv.org/abs/2201.01863) +One other framework for FPGAs that focuses on a holistic approach is CFU Playground (Prakash et al. ([2022](https://arxiv.org/abs/2201.01863))) ### Hardware Built Around Software -In a contrasting approach, hardware can be custom-designed around software requirements to optimize the performance for a specific application. This paradigm creates specialized hardware to better adapt to the specifics of the software, thus reducing computational overhead and improving operational efficiency. One example of this approach is a voice-recognition application by [1](https://www.mdpi.com/2076-3417/11/22/11073). The paper proposes a structure wherein preprocessing operations, traditionally handled by software, are allocated to custom-designed hardware. This technique was achieved by introducing resistor–transistor logic to an inter-integrated circuit sound module for windowing and audio raw data acquisition in the voice-recognition application. Consequently, this offloading of preprocessing operations led to a reduction in computational load on the software, showcasing a practical application of building hardware around software to enhance the efficiency and performance. [1](https://www.mdpi.com/2076-3417/11/22/11073) +In a contrasting approach, hardware can be custom-designed around software requirements to optimize the performance for a specific application. This paradigm creates specialized hardware to better adapt to the specifics of the software, thus reducing computational overhead and improving operational efficiency. One example of this approach is a voice-recognition application by (Kwon et al. ([2021](https://www.mdpi.com/2076-3417/11/22/11073))). The paper proposes a structure wherein preprocessing operations, traditionally handled by software, are allocated to custom-designed hardware. This technique was achieved by introducing resistor–transistor logic to an inter-integrated circuit sound module for windowing and audio raw data acquisition in the voice-recognition application. Consequently, this offloading of preprocessing operations led to a reduction in computational load on the software, showcasing a practical application of building hardware around software to enhance the efficiency and performance. -![A diagram showing how an FPGA was used to offload data preprocessing of the general purpose computation unit. [1](https://www.mdpi.com/2076-3417/11/22/11073)](images/modeloptimization_preprocessor.png) +![A diagram showing how an FPGA was used to offload data preprocessing of the general purpose computation unit. (Kwon et al. ([2021](https://www.mdpi.com/2076-3417/11/22/11073)))](images/modeloptimization_preprocessor.png) ### SplitNets -SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has it's own chip. [1](https://arxiv.org/pdf/2204.04705.pdf) +SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has its own chip. (Dong et al. ([2022](https://arxiv.org/pdf/2204.04705.pdf))) -![A chart showing a comparison between the performance of SplitNets vs all on sensor and all on aggregator approaches. [1](https://arxiv.org/pdf/2204.04705.pdf)](images/modeloptimization_SplitNets.png) +![A chart showing a comparison between the performance of SplitNets vs all on sensor and all on aggregator approaches. (Dong et al. ([2022](https://arxiv.org/pdf/2204.04705.pdf)))](images/modeloptimization_SplitNets.png) ### Hardware Specific Data Augmentation Each edge device may possess unique sensor characteristics, leading to specific noise patterns that can impact model performance. One example is audio data, where variations stemming from the choice of microphone are prevalent. Applications such as Keyword Spotting can experience substantial enhancements by incorporating data recorded from devices similar to those intended for deployment. Fine-tuning of existing models can be employed to adapt the data precisely to the sensor's distinctive characteristics. -### Software and Framework Support +## Software and Framework Support While all of the aforementioned techniques like [pruning](#sec-pruning), [quantization](#sec-quant), and efficient numerics are well-known, they would remain impractical and inaccessible without extensive software support. For example, directly quantizing weights and activations in a model would require manually modifying the model definition and inserting quantization operations throughout. Similarly, directly pruning model weights requires manipulating weight tensors. Such tedious approaches become infeasible at scale. @@ -796,7 +798,7 @@ Automated optimization tools provided by frameworks can analyze models and autom - [Pruning](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras) - Automatically removes unnecessary connections in a model based on analysis of weight importance. Can prune entire filters in convolutional layers or attention heads in transformers. Handles iterative re-training to recover any accuracy loss. - [GraphOptimizer](https://www.tensorflow.org/guide/graph_optimization) - Applies graph optimizations like operator fusion to consolidate operations and reduce execution latency, especially for inference. -![Before/after diagram showing GraphOptimizer fusing operators in a sample graph](images/modeloptimization_graph_optimization.png) +![Before/after diagram showing GraphOptimizer fusing operators in a sample graph](https://www.researchgate.net/publication/347955230/figure/fig5/AS:975063935365122@1609484766968/shows-an-example-of-how-a-graph-could-be-optimized-by-the-mapping-toolchain-In-the.png) These automated modules only require the user to provide the original floating point model, and handle the end-to-end optimization pipeline including any re-training to regain accuracy. Other frameworks like PyTorch also offer increasing automation support, for example through torch.quantization.quantize\_dynamic. Automated optimization makes efficient ML accessible to practitioners without optimization expertise. @@ -809,7 +811,7 @@ Quantization: For example, TensorRT and TensorFlow Lite both support quantizatio Kernel Optimization: For instance, TensorRT does auto-tuning to optimize CUDA kernels based on the GPU architecture for each layer in the model graph. This extracts maximum throughput. Operator Fusion: TensorFlow XLA does aggressive fusion to create optimized binary for TPUs. On mobile, frameworks like NCNN also support fused operators. - +` Hardware-Specific Code: Libraries are used to generate optimized binary code specialized for the target hardware. For example, [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html) uses Nvidia CUDA/cuDNN libraries which are hand-tuned for each GPU architecture. This hardware-specific coding is key for performance. On tinyML devices, this can mean assembly code optimized for a Cortex M4 CPU for example. Vendors provide CMSIS-NN and other libraries. Data Layout Optimizations - We can efficiently leverage memory hierarchy of hardware like cache and registers through techniques like tensor/weight rearrangement, tiling, and reuse. For example, TensorFlow XLA optimizes buffer layouts to maximize TPU utilization. This helps any memory constrained systems. @@ -824,13 +826,13 @@ Implementing model optimization techniques without visibility into the effects o ##### Sparsity (ADD SOME LINKS INTO HERE) -For example, consider sparsity optimizations. Sparsity visualization tools can provide critical insights into pruned models by mapping out exactly which weights have been removed. For example, sparsity heat maps can use color gradients to indicate the percentage of weights pruned in each layer of a neural network. Layers with higher percentages pruned appear darker. This identifies which layers have been simplified the most by pruning. +For example, consider sparsity optimizations. Sparsity visualization tools can provide critical insights into pruned models by mapping out exactly which weights have been removed. For example, sparsity heat maps can use color gradients to indicate the percentage of weights pruned in each layer of a neural network. Layers with higher percentages pruned appear darker. This identifies which layers have been simplified the most by pruning. (Souza ([2020](https://www.numenta.com/blog/2020/10/30/case-for-sparsity-in-neural-networks-part-2-dynamic-sparsity/))) -[Figure: maybe consider including an example from Wolfram] +![A heat map showing a sparse neural network.](https://www.numenta.com/wp-content/uploads/2020/10/Picture1.png) Trend plots can also track sparsity over successive pruning rounds - they may show initial rapid pruning followed by more gradual incremental increases. Tracking the current global sparsity along with statistics like average, minimum, and maximum sparsity per-layer in tables or plots provides an overview of the model composition. For a sample convolutional network, these tools could reveal that the first convolution layer is pruned 20% while the final classifier layer is pruned 70% given its redundancy. The global model sparsity may increase from 10% after initial pruning to 40% after five rounds. -[Figure: Line graph with one line per layer, showing sparsity % over multiple pruning rounds or something to that effet] +![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations (Kung et al. ([2018](https://arxiv.org/abs/1811.04770)))](images/modeloptimization_sparsity.png) By making sparsity data visually accessible, practitioners can better understand exactly how their model is being optimized and which areas are being impacted. The visibility enables them to fine-tune and control the pruning process for a given architecture. @@ -840,11 +842,11 @@ Sparsity visualization turns pruning into a transparent technique instead of a b Converting models to lower numeric precisions through quantization introduces errors that can impact model accuracy if not properly tracked and addressed. Visualizing quantization error distributions provides valuable insights into the effects of reduced precision numerics applied to different parts of a model. For this, histograms of the quantization errors for weights and activations can be generated. These histograms can reveal the shape of the error distribution - whether they resemble a Gaussian distribution or contain significant outliers and spikes. Large outliers may indicate issues with particular layers handling the quantization. Comparing the histograms across layers highlights any problem areas standing out with abnormally high errors. -[Figure: include the example of the histograms, this stuff exists in papers] +![A smooth histogram of quantization error. (Kuzmin et al. ([2021](https://arxiv.org/pdf/2208.09225.pdf)))](images/modeloptimization_quant_hist.png) -Activation visualizations are also important to detect overflow issues. By color mapping the activations before and after quantization, any values pushed outside the intended ranges become visible. This reveals saturation and truncation issues that could skew the information flowing through the model. Detecting these errors allows recalibrating activations to prevent loss of information. +Activation visualizations are also important to detect overflow issues. By color mapping the activations before and after quantization, any values pushed outside the intended ranges become visible. This reveals saturation and truncation issues that could skew the information flowing through the model. Detecting these errors allows recalibrating activations to prevent loss of information. (Mandal ([2022](https://medium.com/exemplifyml-ai/visualizing-neural-network-activation-a27caa451ff))) -[Figure: include a color mapping example] +![Color mapping of activations.](https://compsci697l.github.io/assets/cnnvis/filt1.jpeg) Other techniques, such as tracking the overall mean square quantization error at each step of the quantization-aware training process identifies fluctuations and divergences. Sudden spikes in the tracking plot may indicate points where quantization is disrupting the model training. Monitoring this metric builds intuition on model behavior under quantization. Together these techniques turn quantization into a transparent process. The empirical insights enable practitioners to properly assess quantization effects. They pinpoint areas of the model architecture or training process to recalibrate based on observed quantization issues. This helps achieve numerically stable and accurate quantized models. @@ -860,7 +862,7 @@ TensorFlow Lite - TensorFlow's platform to convert models to a lightweight forma ONNX Runtime - Performs model conversion and inference for models in the open ONNX model format. Provides optimized kernels, supports hardware accelerators like GPUs, and cross-platform deployment from cloud to edge. Allows framework-agnostic deployment. -[add figure of ONNX being an interoperable framework] +![Interoperablily of ONNX](https://miro.medium.com/v2/resize:fit:1400/1*3N6uPaLNEYDjtWBW1vdNoQ.jpeg) PyTorch Mobile - Enables PyTorch models to be run on iOS and Android by converting to mobile-optimized representations. Provides efficient mobile implementations of ops like convolution and special functions optimized for mobile hardware. From 0d1cda27737c3f66cde7f3cbbe468145e375a9f7 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Tue, 31 Oct 2023 09:18:59 -0400 Subject: [PATCH 07/12] Updating references --- optimizations.qmd | 57 ++++++++++++++++++++++++++++++----------------- references.bib | 54 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 91 insertions(+), 20 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index f4038192..c91bc7eb 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -17,7 +17,7 @@ Going one level lower, in @sec-model_ops_numerics, we study the role of numerica Finally, as we go lower closer to the hardware, in @sec-model_ops_hw, we will navigate through the landscape of hardware-software co-design, exploring how models can be optimized by tailoring them to the specific characteristics and capabilities of the target hardware. We will discuss how models can be adapted to exploit the available hardware resources effectively. -![A visualization showing each of the three sections to be covered on the hardware-software gradient.](images/modeloptimization_structure.png) +![A visualization showing each of the three sections to be covered on the hardware-software gradient.](images/modeloptimization_structure.png){width=50%} ## Efficient Model Representation {#sec-model_ops_representation} @@ -130,11 +130,11 @@ The following compact table provides a concise comparison between structured and Pruning has evolved from a purely post-training technique that came at the cost of some accuracy, to a powerful meta-learning approach applied during training to reduce model complexity. This advancement in turn improves compute, memory, and latency efficiency at both training and inference. -A breakthrough finding that catalyzed this evolution was the [lottery ticket hypothesis](https://arxiv.org/abs/1803.03635) empirically discovered by Jonathan Frankle and Michael Carbin. Their work states that within dense neural networks, there exist sparse subnetworks, referred to as "winning tickets," that can match or even exceed the performance of the original model when trained in isolation. Specifically, these winning tickets, when initialized using the same weights as the original network, can achieve similarly high training convergence and accuracy on a given task. It is worthwhile pointing out that they empirically discovered the lottery ticket hypothesis, which was later formalized. +A breakthrough finding that catalyzed this evolution was the [lottery ticket hypothesis](https://arxiv.org/abs/1803.03635) by @frankle_lottery_2019. They empirically discovered by Jonathan Frankle and Michael Carbin. Their work states that within dense neural networks, there exist sparse subnetworks, referred to as "winning tickets," that can match or even exceed the performance of the original model when trained in isolation. Specifically, these winning tickets, when initialized using the same weights as the original network, can achieve similarly high training convergence and accuracy on a given task. It is worthwhile pointing out that they empirically discovered the lottery ticket hypothesis, which was later formalized. -More formally, the lottery ticket hypothesis is a concept in deep learning that suggests that within a neural network, there exist sparse subnetworks (or "winning tickets") that, when initialized with the right weights, are capable of achieving high training convergence and inference performance on a given task. The intuition behind this hypothesis is that, during the training process of a neural network, many neurons and connections become redundant or unimportant, particularly with the inclusion of training techniques encouraging redundancy like dropout. Identifying, pruning out, and initializing these "winning tickets'' allows for faster training and more efficient models, as they contain the essential model decision information for the task. Furthermore, as generally known with the bias-variance tradeoff theory, these tickets suffer less from overparameterization and thus generalize better rather than overfitting to the task. ![](RackMultipart20231019-1-ips613_html_8215915cde9ac59.png) +More formally, the lottery ticket hypothesis is a concept in deep learning that suggests that within a neural network, there exist sparse subnetworks (or "winning tickets") that, when initialized with the right weights, are capable of achieving high training convergence and inference performance on a given task. The intuition behind this hypothesis is that, during the training process of a neural network, many neurons and connections become redundant or unimportant, particularly with the inclusion of training techniques encouraging redundancy like dropout. Identifying, pruning out, and initializing these "winning tickets'' allows for faster training and more efficient models, as they contain the essential model decision information for the task. Furthermore, as generally known with the bias-variance tradeoff theory, these tickets suffer less from overparameterization and thus generalize better rather than overfitting to the task. -![An example experiment from the lottery ticket hypothesis showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios: note the first plot showing how pruning is able to reveal a subnetwork nearly one-fifth the size that trains to a higher test accuracy faster than the unpruned network. However, further note how in the second plot that further pruned models in models that both train slower and are not able to achieve that same maximal test accuracy due to the lower number of parameters (@frankle_lottery_2019).](images/modeloptimization_lottery_ticket_hypothesis.png) +![An example experiment from the lottery ticket hypothesis showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios: note the first plot showing how pruning is able to reveal a subnetwork nearly one-fifth the size that trains to a higher test accuracy faster than the unpruned network. However, further note how in the second plot that further pruned models in models that both train slower and are not able to achieve that same maximal test accuracy due to the lower number of parameters.](images/modeloptimization_lottery_ticket_hypothesis.png) #### Challenges & Limitations @@ -174,7 +174,7 @@ Model compression techniques are crucial for deploying deep learning models on r #### Knowledge Distillation {#sec-kd} -One popular technique is knowledge distillation (KD), which transfers knowledge from a large, complex "teacher" model to a smaller "student" model. The key idea is to train the student model to mimic the teacher's outputs.The concept of KD was first popularized by the work of Geoffrey Hinton, Oriol Vinyals, and Jeff Dean in their paper ["Distilling the Knowledge in a Neural Network" (2015)](https://arxiv.org/abs/1503.02531). +One popular technique is knowledge distillation (KD), which transfers knowledge from a large, complex "teacher" model to a smaller "student" model. The key idea is to train the student model to mimic the teacher's outputs. The concept of KD was first popularized by @hinton2015distilling. ##### Overview and Benefits @@ -206,7 +206,7 @@ One of the seminal works in the realm of matrix factorization, particularly in t The main advantage of low-rank matrix factorization lies in its ability to reduce data dimensionality as shown in the image below where there are fewer parameters to store, making it computationally more efficient and reducing storage requirements at the cost of some additional compute. This can lead to faster computations and more compact data representations, which is especially valuable when dealing with large datasets. Additionally, it may aid in noise reduction and can reveal underlying patterns and relationships in the data. -![A visualization showing the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix $M$ can be approximated by the product of matrices $L_k$ and $R_k^T$. For intuition, most fully connected layers in networks are stored as a projection matrix $M$, which requires $m \times n$ parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store $m \times k + k\times n$ parameters in terms of storage while incurring an additional compute cost of the matrix multiplication. So long as $k \< n/2$, this factorization has fewer parameters total to store while adding a computation of runtime $O(mkn)$ (@noauthor_deep_nodate).](images/modeloptimization_low_rank_matrix_factorization.png) +![A visualization showing the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix $M$ can be approximated by the product of matrices $L_k$ and $R_k^T$. For intuition, most fully connected layers in networks are stored as a projection matrix $M$, which requires $m \times n$ parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store $m \times k + k\times n$ parameters in terms of storage while incurring an additional compute cost of the matrix multiplication. So long as $k < n/2$, this factorization has fewer parameters total to store while adding a computation of runtime $O(mkn)$ (@noauthor_deep_nodate).](images/modeloptimization_low_rank_matrix_factorization.png) ##### Challenges @@ -242,11 +242,19 @@ One edge friendly architecture design is depthwise separable convolutions. Commo #### Example Model Architectures -In this vein, a number of recent architectures have been, from inception, specifically designed for maximizing accuracy on an edge deployment, notably SqueezeNet, MobileNet, and EfficientNet. [SqueezeNet]([https://arxiv.org/abs/1602.07360](https://arxiv.org/abs/1602.07360)), for instance, utilizes a compact architecture with 1x1 convolutions and fire modules to minimize the number of parameters while maintaining strong accuracy. [MobileNet]([https://arxiv.org/abs/1704.04861](https://arxiv.org/abs/1704.04861)), on the other hand, employs the aforementioned depthwise separable convolutions to reduce both computation and model size. [EfficientNet]([https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)) takes a different approach by optimizing network scaling (i.e. varying the depth, width and resolution of a network) and compound scaling, a more nuanced variation network scaling, to achieve superior performance with fewer parameters. These models are essential in the context of edge computing where limited processing power and memory require lightweight yet effective models that can efficiently perform tasks such as image recognition, object detection, and more. Their design principles showcase the importance of intentionally tailored model architecture for edge computing, where performance and efficiency must fit within constraints. +In this vein, a number of recent architectures have been, from inception, specifically designed for maximizing accuracy on an edge deployment, notably SqueezeNet, MobileNet, and EfficientNet. + +* [SqueezeNet]([https://arxiv.org/abs/1602.07360](https://arxiv.org/abs/1602.07360)) by @iandola2016squeezenet for instance, utilizes a compact architecture with 1x1 convolutions and fire modules to minimize the number of parameters while maintaining strong accuracy. + +* [MobileNet]([https://arxiv.org/abs/1704.04861](https://arxiv.org/abs/1704.04861)) by @howard2017mobilenets, on the other hand, employs the aforementioned depthwise separable convolutions to reduce both computation and model size. + +* [EfficientNet]([https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)) by @tan2020efficientnet takes a different approach by optimizing network scaling (i.e. varying the depth, width and resolution of a network) and compound scaling, a more nuanced variation network scaling, to achieve superior performance with fewer parameters. + +These models are essential in the context of edge computing where limited processing power and memory require lightweight yet effective models that can efficiently perform tasks such as image recognition, object detection, and more. Their design principles showcase the importance of intentionally tailored model architecture for edge computing, where performance and efficiency must fit within constraints. #### Streamlining Model Architecture Search -Finally, systematized pipelines for searching for performant edge-compatible model architectures are possible through frameworks like [TinyNAS](https://arxiv.org/abs/2007.10319) and [MorphNet]([https://arxiv.org/abs/1711.06798](https://arxiv.org/abs/1711.06798)). +Finally, systematized pipelines for searching for performant edge-compatible model architectures are possible through frameworks like [TinyNAS](https://arxiv.org/abs/2007.10319) by @lin2020mcunet and [MorphNet]([https://arxiv.org/abs/1711.06798](https://arxiv.org/abs/1711.06798)) by @gordon2018morphnet. TinyNAS is an innovative neural architecture search framework introduced in the MCUNet paper, designed to efficiently discover lightweight neural network architectures for edge devices with limited computational resources. Leveraging reinforcement learning and a compact search space of micro neural modules, TinyNAS optimizes for both accuracy and latency, enabling the deployment of deep learning models on microcontrollers, IoT devices, and other resource-constrained platforms. Specifically, TinyNAS, in conjunction with a network optimizer TinyEngine, generates different search spaces by scaling the input resolution and the model width of a model, then collects the computation FLOPs distribution of satisfying networks within the search space to evaluate its priority. TinyNAS relies on the assumption that a search space that accommodates higher FLOPs under memory constraint can produce higher accuracy models, something that the authors verified in practice in their work. In empirical performance, TinyEngine reduced the peak memory usage of models by around 3.4 times and accelerated inference by 1.7 to 3.3 times compared to [TFLite]([https://www.tensorflow.org/lite](https://www.tensorflow.org/lite)) and [CMSIS-NN]([https://www.keil.com/pack/doc/CMSIS/NN/html/index.html](https://www.keil.com/pack/doc/CMSIS/NN/html/index.html)).. @@ -265,7 +273,7 @@ The imperative for efficient numerics representation arises, particularly as eff Beyond minimizing memory demands, the tremendous potential of efficient numerics representation lies in but is not limited to these fundamental ways. By diminishing computational intensity, efficient numerics can thereby amplify computational speed, allowing more complex models to compute on low-powered devices. Reducing the bit precision of weights and activations on heavily over-parameterized models enables condensation of model size for edge devices without significantly harming the model's predictive accuracy. With the omnipresence of neural networks in models, efficient numerics has a unique advantage in leveraging the layered structure of NNs to vary numeric precision across layers, minimizing precision in resistant layers while preserving higher precision in sensitive layers. -In this segment, we'll delve into how practitioners can harness the principles of hardware-software co-design at the lowest levels of a model to facilitate compatibility with edge devices. Kicking off with an introduction to the numerics, we will examine its implications for device memory and computational complexity. Subsequently, we will embark on a discussion regarding the trade-offs entailed in adopting this strategy, followed by a deep dive into a paramount method of efficient numerics: quantization. +In this section, we will dive into how practitioners can harness the principles of hardware-software co-design at the lowest levels of a model to facilitate compatibility with edge devices. Kicking off with an introduction to the numerics, we will examine its implications for device memory and computational complexity. Subsequently, we will embark on a discussion regarding the trade-offs entailed in adopting this strategy, followed by a deep dive into a paramount method of efficient numerics: quantization. ### The Basics @@ -295,14 +303,15 @@ Precision, delineating the exactness with which a number is represented, bifurca **Integer:** Integer representations are made using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: either +1 or -1. -| **Precision** | **Pros** | **Cons** | -|------------|--------------------------------------------------|--------------------------------------------------| -| **FP32** (Floating Point 32-bit) | - Standard precision used in most deep learning frameworks.\ - High accuracy due to ample representational capacity.\ - Well-suited for training. | - High memory usage.\ - Slower inference times compared to quantized models.\ - Higher energy consumption. | -| **FP16** (Floating Point 16-bit) | - Reduces memory usage compared to FP32.\ - Speeds up computations on hardware that supports FP16.\ - Often used in mixed-precision training to balance speed and accuracy. | - Lower representational capacity compared to FP32.\ - Risk of numerical instability in some models or layers. | -| **INT8** (8-bit Integer) | - Significantly reduced memory footprint compared to floating-point representations.\ - Faster inference if hardware supports INT8 computations.\ - Suitable for many post-training quantization scenarios. | - Quantization can lead to some accuracy loss.\ - Requires careful calibration during quantization to minimize accuracy degradation. | -| **INT4** (4-bit Integer) | - Even lower memory usage than INT8.\ - Further speed-up potential for inference. | - Higher risk of accuracy loss compared to INT8.\ - Calibration during quantization becomes more critical. | -| **Binary** | - Minimal memory footprint (only 1 bit per parameter).\ - Extremely fast inference due to bitwise operations.\ - Power efficient. | - Significant accuracy drop for many tasks.\ - Complex training dynamics due to extreme quantization. | -| **Ternary** | - Low memory usage but slightly more than binary.\ - Offers a middle ground between representation and efficiency. | - Accuracy might still be lower than higher precision models.\ - Training dynamics can be complex. | +| **Precision** | **Pros** | **Cons** | +|---------------------------------------|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------| +| **FP32** (Floating Point 32-bit) | • Standard precision used in most deep learning frameworks.
      • High accuracy due to ample representational capacity.
      • Well-suited for training. | • High memory usage.
      • Slower inference times compared to quantized models.
      • Higher energy consumption. | +| **FP16** (Floating Point 16-bit) | • Reduces memory usage compared to FP32.
      • Speeds up computations on hardware that supports FP16.
      • Often used in mixed-precision training to balance speed and accuracy. | • Lower representational capacity compared to FP32.
      • Risk of numerical instability in some models or layers. | +| **INT8** (8-bit Integer) | • Significantly reduced memory footprint compared to floating-point representations.
      • Faster inference if hardware supports INT8 computations.
      • Suitable for many post-training quantization scenarios. | • Quantization can lead to some accuracy loss.
      • Requires careful calibration during quantization to minimize accuracy degradation. | +| **INT4** (4-bit Integer) | • Even lower memory usage than INT8.
      • Further speed-up potential for inference. | • Higher risk of accuracy loss compared to INT8.
      • Calibration during quantization becomes more critical. | +| **Binary** | • Minimal memory footprint (only 1 bit per parameter).
      • Extremely fast inference due to bitwise operations.
      • Power efficient. | • Significant accuracy drop for many tasks.
      • Complex training dynamics due to extreme quantization. | +| **Ternary** | • Low memory usage but slightly more than binary.
      • Offers a middle ground between representation and efficiency. | • Accuracy might still be lower than higher precision models.
      • Training dynamics can be complex. | + #### Numeric Encoding and Storage @@ -463,7 +472,7 @@ As discussed, some precision in the real value is lost by quantization. In this Non-uniform quantization, on the other hand, does not maintain a consistent interval between quantized values. This approach might be used to allocate more possible discrete values in regions where the parameter values are more densely populated, thereby preserving more detail where it is most needed. For instance, in bell-shaped distributions of weights with long tails, a set of weights in a model predominantly lies within a certain range; thus, more quantization levels might be allocated to that range to preserve finer details, enabling us to better capture information. However, one major weakness of non-uniform quantization is that it requires dequantization before higher precision computations due to its non-uniformity, restricting its ability to accelerate computation compared to uniform quantization. -Typically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, [recent work](https://arxiv.org/abs/1802.00150) formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer Q are adjusted to minimize the difference between the original tensor and quantized counterpart. +Typically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, [recent work](https://arxiv.org/abs/1802.00150) by @xu2018alternating formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer Q are adjusted to minimize the difference between the original tensor and quantized counterpart. $$ \min_Q ||Q(r)-r||^2 @@ -540,13 +549,21 @@ Between the two, calculating the range dynamically usually is very costly, so mo The two prevailing techniques for quantizing models are Post Training Quantization and Quantization Aware Training. -**Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). +**Post Training Quantization:** Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. + +However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. + +Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). ![Table showing the differences between different precisions of quantization.](images/efficientnumerics_lecturenote.png) ![In PTQ, a pretrained model is calibrated using calibration data (e.g., a small subset of training data) to compute the clipping ranges and scaling factors (@surveyofquant).](images/efficientnumerics_PTQ.png) -**Quantization Aware Training** - Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization Aware Training (QAT) [Quantizing deep convolutional networks for efficient inference](2018)([https://arxiv.org/abs/1806.08342](https://arxiv.org/abs/1806.08342)).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. +**Quantization Aware Training:** Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. + +Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization Aware Training (QAT) [Quantizing deep convolutional networks for efficient inference](2018)([https://arxiv.org/abs/1806.08342](https://arxiv.org/abs/1806.08342)). + +To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. ![In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation. Note: the calibration process is often conducted in parallel with the finetuning process for QAT (@surveyofquant).](images/efficientnumerics_QAT.png) diff --git a/references.bib b/references.bib index f73b5933..ffda6029 100644 --- a/references.bib +++ b/references.bib @@ -14,6 +14,60 @@ @article{banbury2020benchmarking year={2020} } +@misc{hinton2015distilling, + title={Distilling the Knowledge in a Neural Network}, + author={Geoffrey Hinton and Oriol Vinyals and Jeff Dean}, + year={2015}, + eprint={1503.02531}, + archivePrefix={arXiv}, + primaryClass={stat.ML} +} + +@inproceedings{gordon2018morphnet, + title={Morphnet: Fast \& simple resource-constrained structure learning of deep networks}, + author={Gordon, Ariel and Eban, Elad and Nachum, Ofir and Chen, Bo and Wu, Hao and Yang, Tien-Ju and Choi, Edward}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={1586--1595}, + year={2018} +} + + +@article{lin2020mcunet, + title={Mcunet: Tiny deep learning on iot devices}, + author={Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Gan, Chuang and Han, Song and others}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={11711--11722}, + year={2020} +} + + +@article{iandola2016squeezenet, + title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size}, + author={Iandola, Forrest N and Han, Song and Moskewicz, Matthew W and Ashraf, Khalid and Dally, William J and Keutzer, Kurt}, + journal={arXiv preprint arXiv:1602.07360}, + year={2016} +} + + +@misc{tan2020efficientnet, + title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, + author={Mingxing Tan and Quoc V. Le}, + year={2020}, + eprint={1905.11946}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@misc{howard2017mobilenets, + title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications}, + author={Andrew G. Howard and Menglong Zhu and Bo Chen and Dmitry Kalenichenko and Weijun Wang and Tobias Weyand and Marco Andreetto and Hartwig Adam}, + year={2017}, + eprint={1704.04861}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + @inproceedings{hendrycks2021natural, title={Natural adversarial examples}, author={Hendrycks, Dan and Zhao, Kevin and Basart, Steven and Steinhardt, Jacob and Song, Dawn}, From aac03787853351aca402e5998d0abfa764c71393 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Tue, 31 Oct 2023 09:41:37 -0400 Subject: [PATCH 08/12] Sample fix of references --- optimizations.qmd | 6 ++---- references.bib | 38 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 40 insertions(+), 4 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index c91bc7eb..335a523f 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -645,13 +645,11 @@ Focusing only on the accuracy when performing Neural Architecture Search leads t #### Single Target, Fixed Platfrom Configuration -The goal here is to find the best architecture in terms of accuracy and hardware efficiency for one fixed target hardware. For a specific hardware, the Arduino Nicla Vision for example, this category of HW-NAS will look for the architecture that optimizes accuracy, latency, energy consumption, … - -Two approaches fall under this category +The goal here is to find the best architecture in terms of accuracy and hardware efficiency for one fixed target hardware. For a specific hardware, the Arduino Nicla Vision for example, this category of HW-NAS will look for the architecture that optimizes accuracy, latency, energy consumption, etc. ##### Hardware-aware Search Strategy -Here, the search is a multi-objective optimization problem, where both the accuracy and hardware cost guide the searching algorithm to find the most efficient architecture. (Tan et al. ([2019](https://openaccess.thecvf.com/content_CVPR_2019/html/Tan_MnasNet_Platform-Aware_Neural_Architecture_Search_for_Mobile_CVPR_2019_paper.html)))(Cai et al. ([2018](https://arxiv.org/abs/1812.00332)))(Wu et al. ([2018](https://arxiv.org/abs/1812.03443))) +Here, the search is a multi-objective optimization problem, where both the accuracy and hardware cost guide the searching algorithm to find the most efficient architecture [@tan2019mnasnet; @cai2018proxylessnas; @wu2019fbnet]. ##### Hardware-aware Search Space diff --git a/references.bib b/references.bib index ffda6029..9c93f995 100644 --- a/references.bib +++ b/references.bib @@ -41,6 +41,44 @@ @article{lin2020mcunet year={2020} } +@inproceedings{tan2019mnasnet, + title={Mnasnet: Platform-aware neural architecture search for mobile}, + author={Tan, Mingxing and Chen, Bo and Pang, Ruoming and Vasudevan, Vijay and Sandler, Mark and Howard, Andrew and Le, Quoc V}, + booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, + pages={2820--2828}, + year={2019} +} + +@article{cai2018proxylessnas, + title={Proxylessnas: Direct neural architecture search on target task and hardware}, + author={Cai, Han and Zhu, Ligeng and Han, Song}, + journal={arXiv preprint arXiv:1812.00332}, + year={2018} +} + +@inproceedings{wu2019fbnet, + title={Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search}, + author={Wu, Bichen and Dai, Xiaoliang and Zhang, Peizhao and Wang, Yanghan and Sun, Fei and Wu, Yiming and Tian, Yuandong and Vajda, Peter and Jia, Yangqing and Keutzer, Kurt}, + booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, + pages={10734--10742}, + year={2019} +} + + +@article{xu2018alternating, + title={Alternating multi-bit quantization for recurrent neural networks}, + author={Xu, Chen and Yao, Jianqiang and Lin, Zhouchen and Ou, Wenwu and Cao, Yuanbin and Wang, Zhirong and Zha, Hongbin}, + journal={arXiv preprint arXiv:1802.00150}, + year={2018} +} + +@article{krishnamoorthi2018quantizing, + title={Quantizing deep convolutional networks for efficient inference: A whitepaper}, + author={Krishnamoorthi, Raghuraman}, + journal={arXiv preprint arXiv:1806.08342}, + year={2018} +} + @article{iandola2016squeezenet, title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size}, From 48daa602b0f76ea65b49327e8b188ec39e48fe93 Mon Sep 17 00:00:00 2001 From: Jayson Lin Date: Tue, 31 Oct 2023 10:00:16 -0400 Subject: [PATCH 09/12] added in-text references and corrected equations --- optimizations.qmd | 26 +++++++++++++------------- references.bib | 33 +++++++++++++++++++++++++++------ 2 files changed, 40 insertions(+), 19 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index e32681f3..42a258d0 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -297,12 +297,12 @@ Precision, delineating the exactness with which a number is represented, bifurca | **Precision** | **Pros** | **Cons** | |------------|--------------------------------------------------|--------------------------------------------------| -| **FP32** (Floating Point 32-bit) | - Standard precision used in most deep learning frameworks.\ - High accuracy due to ample representational capacity.\ - Well-suited for training. | - High memory usage.\ - Slower inference times compared to quantized models.\ - Higher energy consumption. | -| **FP16** (Floating Point 16-bit) | - Reduces memory usage compared to FP32.\ - Speeds up computations on hardware that supports FP16.\ - Often used in mixed-precision training to balance speed and accuracy. | - Lower representational capacity compared to FP32.\ - Risk of numerical instability in some models or layers. | -| **INT8** (8-bit Integer) | - Significantly reduced memory footprint compared to floating-point representations.\ - Faster inference if hardware supports INT8 computations.\ - Suitable for many post-training quantization scenarios. | - Quantization can lead to some accuracy loss.\ - Requires careful calibration during quantization to minimize accuracy degradation. | -| **INT4** (4-bit Integer) | - Even lower memory usage than INT8.\ - Further speed-up potential for inference. | - Higher risk of accuracy loss compared to INT8.\ - Calibration during quantization becomes more critical. | -| **Binary** | - Minimal memory footprint (only 1 bit per parameter).\ - Extremely fast inference due to bitwise operations.\ - Power efficient. | - Significant accuracy drop for many tasks.\ - Complex training dynamics due to extreme quantization. | -| **Ternary** | - Low memory usage but slightly more than binary.\ - Offers a middle ground between representation and efficiency. | - Accuracy might still be lower than higher precision models.\ - Training dynamics can be complex. | +| **FP32** (Floating Point 32-bit) | - Standard precision used in most deep learning frameworks.
      - High accuracy due to ample representational capacity.
      - Well-suited for training. | - High memory usage.
      - Slower inference times compared to quantized models.
      - Higher energy consumption. | +| **FP16** (Floating Point 16-bit) | - Reduces memory usage compared to FP32.
      - Speeds up computations on hardware that supports FP16.
      - Often used in mixed-precision training to balance speed and accuracy. | - Lower representational capacity compared to FP32.
      - Risk of numerical instability in some models or layers. | +| **INT8** (8-bit Integer) | - Significantly reduced memory footprint compared to floating-point representations.
      - Faster inference if hardware supports INT8 computations.
      - Suitable for many post-training quantization scenarios. | - Quantization can lead to some accuracy loss.
      - Requires careful calibration during quantization to minimize accuracy degradation. | +| **INT4** (4-bit Integer) | - Even lower memory usage than INT8.
      - Further speed-up potential for inference. | - Higher risk of accuracy loss compared to INT8.
      - Calibration during quantization becomes more critical. | +| **Binary** | - Minimal memory footprint (only 1 bit per parameter).
      - Extremely fast inference due to bitwise operations.
      - Power efficient. | - Significant accuracy drop for many tasks.
      - Complex training dynamics due to extreme quantization. | +| **Ternary** | - Low memory usage but slightly more than binary.
      - Offers a middle ground between representation and efficiency. | - Accuracy might still be lower than higher precision models.
      - Training dynamics can be complex. | #### Numeric Encoding and Storage @@ -487,7 +487,7 @@ Zero-shot quantization refers to the process of converting a full-precision deep ### Calibration -Calibration is the process of selecting the most effective clipping range [\alpha, \beta] for weights and activations to be quantized to. For example, consider quantizing activations that originally have a floating-point range between -6 and 6 to 8-bit integers. If you just take the minimum and maximum possible 8-bit integer values (-128 to 127) as your quantization range, it might not be the most effective. Instead, calibration would involve passing a representative dataset then use this observed range for quantization. +Calibration is the process of selecting the most effective clipping range [$\alpha$, $\beta$] for weights and activations to be quantized to. For example, consider quantizing activations that originally have a floating-point range between -6 and 6 to 8-bit integers. If you just take the minimum and maximum possible 8-bit integer values (-128 to 127) as your quantization range, it might not be the most effective. Instead, calibration would involve passing a representative dataset then use this observed range for quantization. There are many calibration methods but a few commonly used include: @@ -503,13 +503,13 @@ Importantly, the quality of calibration can make a difference between a quantize #### Symmetric Quantization -Symmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [\alpha, \beta] where \alpha = -\beta. For example, one symmetrical range would be based on the min/max values of the real values such that: -\alpha = \beta = max(abs(r\_max), abs(r\_min)). +Symmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [$\alpha$, $\beta$] where $\alpha = -\beta$. For example, one symmetrical range would be based on the min/max values of the real values such that: -$\alpha = \beta = max(abs(r_{max}), abs(r_{min}))$. -Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called "zeroing out of the zero point") can lead to reduction in computational cost during inference ["Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation" (2023)]([https://arxiv.org/abs/2004.09602 (@intquantfordeepinf)](https://arxiv.org/abs/2004.09602)) . +Symmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called "zeroing out of the zero point") can lead to reduction in computational cost during inference [(@intquantfordeepinf)](https://arxiv.org/abs/2004.09602) . #### Asymmetric Quantization -Asymmetric quantization maps real values to an asymmetrical clipping range that isn't necessarily centered around 0. It involves choosing a range [\alpha, \beta] where \alpha \neq -\beta. For example, selecting a range based on the minimum and maximum real values, or where \alpha = r\_min and \beta = r\_max, creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn't always zero out the real value zero. +Asymmetric quantization maps real values to an asymmetrical clipping range that isn't necessarily centered around 0. It involves choosing a range [$\alpha$, $\beta$] where $\alpha \neq -\beta$. For example, selecting a range based on the minimum and maximum real values, or where $\alpha = r_{min}$ and $\beta = r_{max}$, creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn't always zero out the real value zero. ![Illustration of symmetric quantization (left) and asymmetric quantization (right). Symmetric quantization maps real values to [-127, 127], and asymmetric maps to [-128, 127] (@surveyofquant).](images/efficientnumerics_symmetry.png) @@ -540,13 +540,13 @@ Between the two, calculating the range dynamically usually is very costly, so mo The two prevailing techniques for quantizing models are Post Training Quantization and Quantization Aware Training. -**Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](2023)(https://arxiv.org/abs/2211.10438). +**Post Training Quantization** - Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy [(@smoothquant)](https://arxiv.org/abs/2211.10438). ![Table showing the differences between different precisions of quantization.](images/efficientnumerics_lecturenote.png) ![In PTQ, a pretrained model is calibrated using calibration data (e.g., a small subset of training data) to compute the clipping ranges and scaling factors (@surveyofquant).](images/efficientnumerics_PTQ.png) -**Quantization Aware Training** - Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization Aware Training (QAT) [Quantizing deep convolutional networks for efficient inference](2018)([https://arxiv.org/abs/1806.08342](https://arxiv.org/abs/1806.08342)).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. +**Quantization Aware Training** - Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization Aware Training (QAT) [(@quantdeep)](https://arxiv.org/abs/1806.08342).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. ![In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation. Note: the calibration process is often conducted in parallel with the finetuning process for QAT (@surveyofquant).](images/efficientnumerics_QAT.png) @@ -577,7 +577,7 @@ Weight Quantization: Involves converting the continuous or high-precision weight ![Diagram of quantizing weights and activations](images/efficientnumerics_weightsactivations.png) -Activation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, [recent work (@awq)](https://arxiv.org/pdf/2306.00978.pdf) has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights. +Activation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, recent work has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights [(@awq)](https://arxiv.org/pdf/2306.00978.pdf). ### Trade-offs diff --git a/references.bib b/references.bib index f73b5933..1d13cb50 100644 --- a/references.bib +++ b/references.bib @@ -1007,14 +1007,18 @@ @misc{surveyofquant url = {https://arxiv.org/abs/2103.13630}, urldate = {2021-06-21}, author = {Gholami and Kim, Dong and Yao, Mahoney and Keutzer}, - year = {2021} + year = {2021}, + doi = {10.48550/arXiv.2103.13630}, + abstract = {As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose. Strongly related to the problem of numerical representation is the problem of quantization: in what manner should a set of continuous real-valued numbers be distributed over a fixed discrete set of numbers to minimize the number of bits required and also to maximize the accuracy of the attendant computations? This perennial problem of quantization is particularly relevant whenever memory and/or computational resources are severely restricted, and it has come to the forefront in recent years due to the remarkable performance of Neural Network models in computer vision, natural language processing, and related areas. Moving from floating-point representations to low-precision fixed integer values represented in four bits or less holds the potential to reduce the memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x to 8x are often realized in practice in these applications. Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks. In this article, we survey approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. With this survey and its organization, we hope to have presented a useful snapshot of the current research in quantization for Neural Networks and to have given an intelligent organization to ease the evaluation of future research in this area.}, } @misc{intquantfordeepinf, title = {Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation)}, url = {https://arxiv.org/abs/2004.09602}, urldate = {2020-04-20}, author = {Wu and Judd, Zhang and Isaev, Micikevicius}, - year = {2020} + year = {2020}, + doi = {10.48550/arXiv.2004.09602}, + abstract = {Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of quantization parameters and evaluate their choices on a wide range of neural network models for different application domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are more difficult to quantize, such as MobileNets and BERT-large.}, } @misc{deci, title = {The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training}, @@ -1022,22 +1026,39 @@ @misc{deci } @misc{awq, title = {AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration}, - url = {https://arxiv.org/pdf/2306.00978.pdf}, + url = {https://arxiv.org/abs/2306.00978}, urldate = {2023-10-03}, author = {Lin and Tang, Tang and Yang, Dang and Gan, Han}, - year = {2023} + year = {2023}, + doi = {10.48550/arXiv.2306.00978}, + abstract = {Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth). In this paper, we propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization. Our method is based on the observation that weights are not equally important: protecting only 1% of salient weights can greatly reduce quantization error. We then propose to search for the optimal perchannel scaling that protects the salient weights by observing the activation, not weights. AWQ does not rely on any backpropagation or reconstruction, so it can well preserve LLMs’ generalization ability on different domains and modalities, without overfitting to the calibration set. AWQ outperforms existing work on various language modeling and domain-specific benchmarks. Thanks to better generalization, it achieves excellent quantization performance for instruction-tuned LMs and, for the first time, multi-modal LMs. Alongside AWQ, we implement an efficient and flexible inference framework tailored for LLMs on the edge, offering more than 3× speedup over the Huggingface FP16 implementation on both desktop and mobile GPUs. It also democratizes the deployment of the 70B Llama-2 model on mobile GPU (NVIDIA Jetson Orin 64GB).}, } @misc{smoothquant, title = {SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models}, url = {https://arxiv.org/abs/2211.10438}, urldate = {2023-06-05}, author = {Xiao and Lin, Seznec and Wu, Demouth and Han}, - year = {2023} + year = {2023}, + doi = {10.48550/arXiv.2211.10438}, + abstract = {Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency at the same time. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the matrix multiplications in LLMs, including OPT, BLOOM, GLM, MT-NLG, and LLaMA family. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. SmoothQuant enables serving 530B LLM within a single node. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs.}, } @misc{deepcompress, title = {Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding}, url = {https://arxiv.org/abs/1510.00149}, urldate = {2016-02-15}, author = {Han and Mao and Dally}, - year = {2016} + year = {2016}, + doi = {10.48550/arXiv.1510.00149}, + abstract = {Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce "deep compression", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency.}, +} +@misc{quantdeep, + title = {Quantizing deep convolutional networks for efficient inference: A whitepaper}, + url = {https://arxiv.org/abs/1806.08342}, + doi = {10.48550/arXiv.1806.08342}, + abstract = {We present an overview of techniques for quantizing convolutional neural networks for inference with integer weights and activations. Per-channel quantization of weights and per-layer quantization of activations to 8-bits of precision post-training produces classification accuracies within 2% of floating point networks for a wide variety of CNN architectures. Model sizes can be reduced by a factor of 4 by quantizing weights to 8-bits, even when 8-bit arithmetic is not supported. This can be achieved with simple, post training quantization of weights.We benchmark latencies of quantized networks on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations compared to floating point on CPUs. Speedups of up to 10x are observed on specialized processors with fixed point SIMD capabilities, like the Qualcomm QDSPs with HVX. Quantization-aware training can provide further improvements, reducing the gap to floating point to 1% at 8-bit precision. Quantization-aware training also allows for reducing the precision of weights to four bits with accuracy losses ranging from 2% to 10%, with higher accuracy drop for smaller networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing convolutional networks and review best practices for quantization-aware training to obtain high accuracy with quantized weights and activations. We recommend that per-channel quantization of weights and per-layer quantization of activations be the preferred quantization scheme for hardware acceleration and kernel optimization. We also propose that future processors and hardware accelerators for optimized inference support precisions of 4, 8 and 16 bits.}, + urldate = {2018-06-21}, + publisher = {arXiv}, + author = {Krishnamoorthi}, + month = jun, + year = {2018}, } \ No newline at end of file From 25757312d2591967d76a748d12e9e38a596fc857 Mon Sep 17 00:00:00 2001 From: Aghyad Deeb Date: Tue, 31 Oct 2023 13:29:06 -0400 Subject: [PATCH 10/12] configured references to bibTex --- optimizations.qmd | 40 ++++++------ references.bib | 163 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 183 insertions(+), 20 deletions(-) diff --git a/optimizations.qmd b/optimizations.qmd index cb38f24f..37532fff 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -632,7 +632,7 @@ Efficient hardware implementation transcends the selection of suitable component Focusing only on the accuracy when performing Neural Architecture Search leads to models that are exponentially complex and require increasing memory and compute. This has lead to hardware constraints limiting the exploitation of the deep learning models at their full potential. Manually designing the architecture of the model is even harder when considering the hardware variety and limitations. This has lead to the creation of Hardware-aware Neural Architecture Search that incorporate the hardware contractions into their search and optimize the search space for a specific hardware and accuracy. HW-NAS can be categorized based how it optimizes for hardware. We will briefly explore these categories and leave links to related papers for the interested reader. -![Taxonomy of HW-NAS (Benmeziane et al. ([2021](https://www.ijcai.org/proceedings/2021/592)))](images/modeloptimization_HW-NAS.png) +![Taxonomy of HW-NAS [@ijcai2021p592]](images/modeloptimization_HW-NAS.png) #### Single Target, Fixed Platfrom Configuration @@ -645,15 +645,15 @@ Here, the search is a multi-objective optimization problem, where both the accur ##### Hardware-aware Search Space -Here, the search space is restricted to the architectures that perform well on the specific hardware. This can be achieved by either measuring the operators (Conv operator, Pool operator, …) performance, or define a set of rules that limit the search space. (Zhang et al. ([2020](https://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.html))) +Here, the search space is restricted to the architectures that perform well on the specific hardware. This can be achieved by either measuring the operators (Conv operator, Pool operator, …) performance, or define a set of rules that limit the search space. [@Zhang_2020_CVPR_Workshops] #### Single Target, Multiple Platform Configurations -Some hardwares may have different configurations. For example, FPGAs have Configurable Logic Blocks (CLBs) that can be configured by the firmware. This method allows for the HW-NAS to explore different configurations. (Jiang et al. ([2019](https://arxiv.org/abs/1901.11211)))(Yang et al. ([2020](https://arxiv.org/abs/2002.04116))) +Some hardwares may have different configurations. For example, FPGAs have Configurable Logic Blocks (CLBs) that can be configured by the firmware. This method allows for the HW-NAS to explore different configurations. [@jiang2019accuracy; @yang2020coexploration] #### Multiple Targets -This category aims at optimizing a single model for multiple hardwares. This can be helpful for mobile devices development as it can optimize to different phones models. (Chu et al. ([2020](https://arxiv.org/abs/2008.08178)))(Jiang et al. ([2020](https://ieeexplore.ieee.org/document/9102721))) +This category aims at optimizing a single model for multiple hardwares. This can be helpful for mobile devices development as it can optimize to different phones models. [@chu2021discovering; @jiang2019accuracy] #### Examples of Hardware-Aware Neural Architecture Search @@ -663,14 +663,14 @@ TinyNAS adopts a two stage approach to finding an optimal architecture for model First, TinyNAS generate multiple search spaces by varying the input resolution of the model, and the number of channels of the layers of the model. Then, TinyNAS chooses a search space based on the FLOPs (Floating Point Operations Per Second) of each search space -Then, TinyNAS performs a search operation on the chosen space to find the optimal architecture for the specific constraints of the microcontroller. (Han et al. ([2020](https://arxiv.org/abs/2007.10319))) +Then, TinyNAS performs a search operation on the chosen space to find the optimal architecture for the specific constraints of the microcontroller. [@lin2020mcunet] -![A diagram showing how search spaces with high probability of finding an architecture with large number of FLOPs provide models with higher accuracy (Han et al. ([2020](https://arxiv.org/abs/2007.10319)))](images/modeloptimization_TinyNAS.png) +![A diagram showing how search spaces with high probability of finding an architecture with large number of FLOPs provide models with higher accuracy [@lin2020mcunet]](images/modeloptimization_TinyNAS.png) #### Topology-Aware NAS -Focuses on creating and optimizing a search space that aligns with the hardware topology of the device. (Zhang et al. ([2019](https://arxiv.org/pdf/1911.09251.pdf))) +Focuses on creating and optimizing a search space that aligns with the hardware topology of the device. [@zhang2019autoshrink] ### Challenges of Hardware-Aware Neural Architecture Search @@ -698,13 +698,13 @@ Similarly to blocking, tiling divides data and computation into chunks, but exte ##### Optimized Kernel Libraries -This comprises developing optimized kernels that take full advantage of a specific hardware. One example is the CMSIS-NN library, which is a collection of efficient neural network kernels developed to optimize the performance and minimize the memory footprint of models on Arm Cortex-M processors, which are common on IoT edge devices. The kernel leverage multiple hardware capabilities of Cortex-M processors like Single Instruction Multple Data (SIMD), Floating Point Units (FPUs) and M-Profile Vector Extensions (MVE). These optimization make common operations like matrix multiplications more efficient, boosting the performance of model operations on Cortex-M processors. (Lai et al. ([2018](https://arxiv.org/abs/1801.06601#:~:text=This%20paper%20presents%20CMSIS,for%20intelligent%20IoT%20edge%20devices))) +This comprises developing optimized kernels that take full advantage of a specific hardware. One example is the CMSIS-NN library, which is a collection of efficient neural network kernels developed to optimize the performance and minimize the memory footprint of models on Arm Cortex-M processors, which are common on IoT edge devices. The kernel leverage multiple hardware capabilities of Cortex-M processors like Single Instruction Multple Data (SIMD), Floating Point Units (FPUs) and M-Profile Vector Extensions (MVE). These optimization make common operations like matrix multiplications more efficient, boosting the performance of model operations on Cortex-M processors. [@lai2018cmsisnn] ### Compute-in-Memory (CiM) -This is one example of Algorithm-Hardware Co-design. CiM is a computing paradigm that performs computation within memory. Therefore, CiM architectures allow for operations to be performed directly on the stored data, without the need to shuttle data back and forth between separate processing and memory units. This design paradigm is particularly beneficial in scenarios where data movement is a primary source of energy consumption and latency, such as in TinyML applications on edge devices. Through algorithm-hardware co-design, the algorithms can be optimized to leverage the unique characteristics of CiM architectures, and conversely, the CiM hardware can be customized or configured to better support the computational requirements and characteristics of the algorithms. This is achieved by using the analog properties of memory cells, such as addition and multiplication in DRAM. (Zhou et al. ([2021](https://arxiv.org/abs/2111.06503))) +This is one example of Algorithm-Hardware Co-design. CiM is a computing paradigm that performs computation within memory. Therefore, CiM architectures allow for operations to be performed directly on the stored data, without the need to shuttle data back and forth between separate processing and memory units. This design paradigm is particularly beneficial in scenarios where data movement is a primary source of energy consumption and latency, such as in TinyML applications on edge devices. Through algorithm-hardware co-design, the algorithms can be optimized to leverage the unique characteristics of CiM architectures, and conversely, the CiM hardware can be customized or configured to better support the computational requirements and characteristics of the algorithms. This is achieved by using the analog properties of memory cells, such as addition and multiplication in DRAM. [@zhou2021analognets] -![A figure showing how Computing in Memory can be used for always-on tasks to offload tasks of the power consuming processing unit [1](https://arxiv.org/abs/2111.06503)](images/modeloptimization_CiM.png) +![A figure showing how Computing in Memory can be used for always-on tasks to offload tasks of the power consuming processing unit [@zhou2021analognets]](images/modeloptimization_CiM.png) ### Memory Access Optimization @@ -712,31 +712,31 @@ Different devices may have different memory hierarchies. Optimizing for the spec ### Leveraging Sparsity -Pruning is a fundamental approach to compress models to make them compatible with resource constrained devices. This results in sparse models where a lot of weights are 0's. Therefore, leveraging this sparsity can lead to significant improvements in performance. Tools were created to achieve exactly this. RAMAN, is a sparseTinyML accelerator designed for inference on edge devices. RAMAN overlap input and output activations on the same memory space, reducing storage requirements by up to 50%. (Krishna et al. ([2023](https://ar5iv.labs.arxiv.org/html/2306.06493))) +Pruning is a fundamental approach to compress models to make them compatible with resource constrained devices. This results in sparse models where a lot of weights are 0's. Therefore, leveraging this sparsity can lead to significant improvements in performance. Tools were created to achieve exactly this. RAMAN, is a sparseTinyML accelerator designed for inference on edge devices. RAMAN overlap input and output activations on the same memory space, reducing storage requirements by up to 50%. [@krishna2023raman] -![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations (Kung et al. ([2018](https://arxiv.org/abs/1811.04770)))](images/modeloptimization_sparsity.png) +![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations. [@kung2018packing] ### Optimization Frameworks Optimization Frameworks have been introduced to exploit the specific capabilities of the hardware to accelerate the software. One example of such a framework is hls4ml. This open-source software-hardware co-design workflow aids in interpreting and translating machine learning algorithms for implementation with both FPGA and ASIC technologies, enhancing their. Features such as network optimization, new Python APIs, quantization-aware pruning, and end-to-end FPGA workflows are embedded into the hls4ml framework, leveraging parallel processing units, memory hierarchies, and specialized instruction sets to optimize models for edge hardware. Moreover, hls4ml is capable of translating machine learning algorithms directly into FPGA firmware. -![A Diagram showing the workflow with the hls4ml framework (Fahim et al. ([2021](https://arxiv.org/pdf/2103.05579.pdf)))](images/modeloptimization_hls4ml.png) +![A Diagram showing the workflow with the hls4ml framework [@fahim2021hls4ml]](images/modeloptimization_hls4ml.png) -One other framework for FPGAs that focuses on a holistic approach is CFU Playground (Prakash et al. ([2022](https://arxiv.org/abs/2201.01863))) +One other framework for FPGAs that focuses on a holistic approach is CFU Playground [@Prakash_2023] ### Hardware Built Around Software -In a contrasting approach, hardware can be custom-designed around software requirements to optimize the performance for a specific application. This paradigm creates specialized hardware to better adapt to the specifics of the software, thus reducing computational overhead and improving operational efficiency. One example of this approach is a voice-recognition application by (Kwon et al. ([2021](https://www.mdpi.com/2076-3417/11/22/11073))). The paper proposes a structure wherein preprocessing operations, traditionally handled by software, are allocated to custom-designed hardware. This technique was achieved by introducing resistor–transistor logic to an inter-integrated circuit sound module for windowing and audio raw data acquisition in the voice-recognition application. Consequently, this offloading of preprocessing operations led to a reduction in computational load on the software, showcasing a practical application of building hardware around software to enhance the efficiency and performance. +In a contrasting approach, hardware can be custom-designed around software requirements to optimize the performance for a specific application. This paradigm creates specialized hardware to better adapt to the specifics of the software, thus reducing computational overhead and improving operational efficiency. One example of this approach is a voice-recognition application by [@app112211073]. The paper proposes a structure wherein preprocessing operations, traditionally handled by software, are allocated to custom-designed hardware. This technique was achieved by introducing resistor–transistor logic to an inter-integrated circuit sound module for windowing and audio raw data acquisition in the voice-recognition application. Consequently, this offloading of preprocessing operations led to a reduction in computational load on the software, showcasing a practical application of building hardware around software to enhance the efficiency and performance. -![A diagram showing how an FPGA was used to offload data preprocessing of the general purpose computation unit. (Kwon et al. ([2021](https://www.mdpi.com/2076-3417/11/22/11073)))](images/modeloptimization_preprocessor.png) +![A diagram showing how an FPGA was used to offload data preprocessing of the general purpose computation unit. [@app112211073]](images/modeloptimization_preprocessor.png) ### SplitNets -SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has its own chip. (Dong et al. ([2022](https://arxiv.org/pdf/2204.04705.pdf))) +SplitNets were introduced in the context of Head-Mounted systems. They distribute the Deep Neural Networks (DNNs) workload among camera sensors and an aggregator. This is particularly compelling the in context of TinyML. The SplitNet framework is a split-aware NAS to find the optimal neural network architecture to achieve good accuracy, split the model among the sensors and the aggregator, and minimize the communication between the sensors and the aggregator. Minimal communication is important in TinyML where memory is highly constrained, this way the sensors conduct some of the processing on their chips and then they send only the necessary information to the aggregator. When testing on ImageNet, SplitNets were able to reduce the latency by one order of magnitude on head-mounted devices. This can be helpful when the sensor has its own chip. [@dong2022splitnets] -![A chart showing a comparison between the performance of SplitNets vs all on sensor and all on aggregator approaches. (Dong et al. ([2022](https://arxiv.org/pdf/2204.04705.pdf)))](images/modeloptimization_SplitNets.png) +![A chart showing a comparison between the performance of SplitNets vs all on sensor and all on aggregator approaches. [@dong2022splitnets]](images/modeloptimization_SplitNets.png) ### Hardware Specific Data Augmentation @@ -803,7 +803,7 @@ For example, consider sparsity optimizations. Sparsity visualization tools can p Trend plots can also track sparsity over successive pruning rounds - they may show initial rapid pruning followed by more gradual incremental increases. Tracking the current global sparsity along with statistics like average, minimum, and maximum sparsity per-layer in tables or plots provides an overview of the model composition. For a sample convolutional network, these tools could reveal that the first convolution layer is pruned 20% while the final classifier layer is pruned 70% given its redundancy. The global model sparsity may increase from 10% after initial pruning to 40% after five rounds. -![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations (Kung et al. ([2018](https://arxiv.org/abs/1811.04770)))](images/modeloptimization_sparsity.png) +![A figure showing the sparse columns of the filter matrix of a CNN that are aggregated to create a dense matrix that, leading to smaller dimensions in the matrix and more efficient computations [@kung2018packing]](images/modeloptimization_sparsity.png) By making sparsity data visually accessible, practitioners can better understand exactly how their model is being optimized and which areas are being impacted. The visibility enables them to fine-tune and control the pruning process for a given architecture. @@ -813,7 +813,7 @@ Sparsity visualization turns pruning into a transparent technique instead of a b Converting models to lower numeric precisions through quantization introduces errors that can impact model accuracy if not properly tracked and addressed. Visualizing quantization error distributions provides valuable insights into the effects of reduced precision numerics applied to different parts of a model. For this, histograms of the quantization errors for weights and activations can be generated. These histograms can reveal the shape of the error distribution - whether they resemble a Gaussian distribution or contain significant outliers and spikes. Large outliers may indicate issues with particular layers handling the quantization. Comparing the histograms across layers highlights any problem areas standing out with abnormally high errors. -![A smooth histogram of quantization error. (Kuzmin et al. ([2021](https://arxiv.org/pdf/2208.09225.pdf)))](images/modeloptimization_quant_hist.png) +![A smooth histogram of quantization error. [@kuzmin2022fp8]](images/modeloptimization_quant_hist.png) Activation visualizations are also important to detect overflow issues. By color mapping the activations before and after quantization, any values pushed outside the intended ranges become visible. This reveals saturation and truncation issues that could skew the information flowing through the model. Detecting these errors allows recalibrating activations to prevent loss of information. (Mandal ([2022](https://medium.com/exemplifyml-ai/visualizing-neural-network-activation-a27caa451ff))) diff --git a/references.bib b/references.bib index 83991aa9..4501f577 100644 --- a/references.bib +++ b/references.bib @@ -1153,4 +1153,167 @@ @misc{quantdeep author = {Krishnamoorthi}, month = jun, year = {2018}, +} +@inproceedings{ijcai2021p592, + title = {Hardware-Aware Neural Architecture Search: Survey and Taxonomy}, + author = {Benmeziane, Hadjer and El Maghraoui, Kaoutar and Ouarnoughi, Hamza and Niar, Smail and Wistuba, Martin and Wang, Naigang}, + booktitle = {Proceedings of the Thirtieth International Joint Conference on + Artificial Intelligence, {IJCAI-21}}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, + editor = {Zhi-Hua Zhou}, + pages = {4322--4329}, + year = {2021}, + month = {8}, + note = {Survey Track}, + doi = {10.24963/ijcai.2021/592}, + url = {https://doi.org/10.24963/ijcai.2021/592}, +} + +@InProceedings{Zhang_2020_CVPR_Workshops, +author = {Zhang, Li Lyna and Yang, Yuqing and Jiang, Yuhang and Zhu, Wenwu and Liu, Yunxin}, +title = {Fast Hardware-Aware Neural Architecture Search}, +booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, +month = {June}, +year = {2020} +} + +@misc{jiang2019accuracy, + title={Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search}, + author={Weiwen Jiang and Xinyi Zhang and Edwin H. -M. Sha and Lei Yang and Qingfeng Zhuge and Yiyu Shi and Jingtong Hu}, + year={2019}, + eprint={1901.11211}, + archivePrefix={arXiv}, + primaryClass={cs.DC} +} + +@misc{yang2020coexploration, + title={Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks}, + author={Lei Yang and Zheyu Yan and Meng Li and Hyoukjun Kwon and Liangzhen Lai and Tushar Krishna and Vikas Chandra and Weiwen Jiang and Yiyu Shi}, + year={2020}, + eprint={2002.04116}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@misc{chu2021discovering, + title={Discovering Multi-Hardware Mobile Models via Architecture Search}, + author={Grace Chu and Okan Arikan and Gabriel Bender and Weijun Wang and Achille Brighton and Pieter-Jan Kindermans and Hanxiao Liu and Berkin Akin and Suyog Gupta and Andrew Howard}, + year={2021}, + eprint={2008.08178}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +@misc{lin2020mcunet, + title={MCUNet: Tiny Deep Learning on IoT Devices}, + author={Ji Lin and Wei-Ming Chen and Yujun Lin and John Cohn and Chuang Gan and Song Han}, + year={2020}, + eprint={2007.10319}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +@misc{zhang2019autoshrink, + title={AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture}, + author={Tunhou Zhang and Hsin-Pai Cheng and Zhenwen Li and Feng Yan and Chengyu Huang and Hai Li and Yiran Chen}, + year={2019}, + eprint={1911.09251}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@misc{lai2018cmsisnn, + title={CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs}, + author={Liangzhen Lai and Naveen Suda and Vikas Chandra}, + year={2018}, + eprint={1801.06601}, + archivePrefix={arXiv}, + primaryClass={cs.NE} +} + +@misc{zhou2021analognets, + title={AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator}, + author={Chuteng Zhou and Fernando Garcia Redondo and Julian Büchel and Irem Boybat and Xavier Timoneda Comas and S. R. Nandakumar and Shidhartha Das and Abu Sebastian and Manuel Le Gallo and Paul N. Whatmough}, + year={2021}, + eprint={2111.06503}, + archivePrefix={arXiv}, + primaryClass={cs.AR} +} + +@misc{krishna2023raman, + title={RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge}, + author={Adithya Krishna and Srikanth Rohit Nudurupati and Chandana D G and Pritesh Dwivedi and André van Schaik and Mahesh Mehendale and Chetan Singh Thakur}, + year={2023}, + eprint={2306.06493}, + archivePrefix={arXiv}, + primaryClass={cs.NE} +} + +@misc{kung2018packing, + title={Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization}, + author={H. T. Kung and Bradley McDanel and Sai Qian Zhang}, + year={2018}, + eprint={1811.04770}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@misc{fahim2021hls4ml, + title={hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices}, + author={Farah Fahim and Benjamin Hawks and Christian Herwig and James Hirschauer and Sergo Jindariani and Nhan Tran and Luca P. Carloni and Giuseppe Di Guglielmo and Philip Harris and Jeffrey Krupa and Dylan Rankin and Manuel Blanco Valentin and Josiah Hester and Yingyi Luo and John Mamish and Seda Orgrenci-Memik and Thea Aarrestad and Hamza Javed and Vladimir Loncar and Maurizio Pierini and Adrian Alan Pol and Sioni Summers and Javier Duarte and Scott Hauck and Shih-Chieh Hsu and Jennifer Ngadiuba and Mia Liu and Duc Hoang and Edward Kreinar and Zhenbin Wu}, + year={2021}, + eprint={2103.05579}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@inproceedings{Prakash_2023, + doi = {10.1109/ispass57527.2023.00024}, + + url = {https://doi.org/10.1109%2Fispass57527.2023.00024}, + + year = 2023, + month = {apr}, + + publisher = {{IEEE} +}, + + author = {Shvetank Prakash and Tim Callahan and Joseph Bushagour and Colby Banbury and Alan V. Green and Pete Warden and Tim Ansell and Vijay Janapa Reddi}, + + title = {{CFU} Playground: Full-Stack Open-Source Framework for Tiny Machine Learning ({TinyML}) Acceleration on {FPGAs}}, + + booktitle = {2023 {IEEE} International Symposium on Performance Analysis of Systems and Software ({ISPASS})} +} + + +@Article{app112211073, +AUTHOR = {Kwon, Jisu and Park, Daejin}, +TITLE = {Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices}, +JOURNAL = {Applied Sciences}, +VOLUME = {11}, +YEAR = {2021}, +NUMBER = {22}, +ARTICLE-NUMBER = {11073}, +URL = {https://www.mdpi.com/2076-3417/11/22/11073}, +ISSN = {2076-3417}, +ABSTRACT = {On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we propose a structure in which the part that preprocesses externally input data in the TinyML application is distributed to the hardware. These processes are performed using software in the microcontroller unit of an edge device. Furthermore, resistor–transistor logic, which perform not only windowing using the Hann function, but also acquire audio raw data, is added to the inter-integrated circuit sound module that collects audio data in the voice-recognition application. As a result of the experiment, the windowing function was excluded from the TinyML application of the embedded board. When the length of the hardware-implemented Hann window is 80 and the quantization degree is 2−5, the exclusion causes a decrease in the execution time of the front-end function and energy consumption by 8.06% and 3.27%, respectively.}, +DOI = {10.3390/app112211073} +} + +@misc{dong2022splitnets, + title={SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems}, + author={Xin Dong and Barbara De Salvo and Meng Li and Chiao Liu and Zhongnan Qu and H. T. Kung and Ziyun Li}, + year={2022}, + eprint={2204.04705}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +@misc{kuzmin2022fp8, + title={FP8 Quantization: The Power of the Exponent}, + author={Andrey Kuzmin and Mart Van Baalen and Yuwei Ren and Markus Nagel and Jorn Peters and Tijmen Blankevoort}, + year={2022}, + eprint={2208.09225}, + archivePrefix={arXiv}, + primaryClass={cs.LG} } \ No newline at end of file From 11e8e31c049bf674d604bcebb89aeb17acf8abe4 Mon Sep 17 00:00:00 2001 From: Jayson Lin Date: Tue, 31 Oct 2023 13:48:19 -0400 Subject: [PATCH 11/12] typo fix --- optimizations.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/optimizations.qmd b/optimizations.qmd index cb38f24f..02c33084 100644 --- a/optimizations.qmd +++ b/optimizations.qmd @@ -506,7 +506,7 @@ Entropy: Use KL divergence to minimize information loss between the original flo Percentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values. -![Histogram of input activatsions to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png) +![Histogram of input activations to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png) Importantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it's an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric. From 721977ec81ed47ec547526bbf2adecce8c5af8d0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 2 Nov 2023 20:01:45 +0000 Subject: [PATCH 12/12] Update readme and contributors.qmd with contributors --- .all-contributorsrc | 146 ++++++++++++++++++++++---------------------- README.md | 30 ++++----- contributors.qmd | 30 ++++----- 3 files changed, 103 insertions(+), 103 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index ec7ff5a1..05e42990 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -7,10 +7,19 @@ ], "contributors": [ { - "login": "ShvetankPrakash", - "name": "Shvetank Prakash", - "avatar_url": "https://avatars.githubusercontent.com/ShvetankPrakash", - "profile": "https://github.com/ShvetankPrakash", + "login": "Mjrovai", + "name": "Marcelo Rovai", + "avatar_url": "https://avatars.githubusercontent.com/Mjrovai", + "profile": "https://github.com/Mjrovai", + "contributions": [ + "doc" + ] + }, + { + "login": "ishapira1", + "name": "ishapira", + "avatar_url": "https://avatars.githubusercontent.com/ishapira1", + "profile": "https://github.com/ishapira1", "contributions": [ "doc" ] @@ -25,118 +34,118 @@ ] }, { - "login": "sjohri20", - "name": "sjohri20", - "avatar_url": "https://avatars.githubusercontent.com/sjohri20", - "profile": "https://github.com/sjohri20", + "login": "18jeffreyma", + "name": "Jeffrey Ma", + "avatar_url": "https://avatars.githubusercontent.com/18jeffreyma", + "profile": "https://github.com/18jeffreyma", "contributions": [ "doc" ] }, { - "login": "jaysonzlin", - "name": "Jayson Lin", - "avatar_url": "https://avatars.githubusercontent.com/jaysonzlin", - "profile": "https://github.com/jaysonzlin", + "login": "uchendui", + "name": "Ikechukwu Uchendu", + "avatar_url": "https://avatars.githubusercontent.com/uchendui", + "profile": "https://github.com/uchendui", "contributions": [ "doc" ] }, { - "login": "BaeHenryS", - "name": "Henry Bae", - "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", - "profile": "https://github.com/BaeHenryS", + "login": "sophiacho1", + "name": "sophiacho1", + "avatar_url": "https://avatars.githubusercontent.com/sophiacho1", + "profile": "https://github.com/sophiacho1", "contributions": [ "doc" ] }, { - "login": "Naeemkh", - "name": "naeemkh", - "avatar_url": "https://avatars.githubusercontent.com/Naeemkh", - "profile": "https://github.com/Naeemkh", + "login": "ShvetankPrakash", + "name": "Shvetank Prakash", + "avatar_url": "https://avatars.githubusercontent.com/ShvetankPrakash", + "profile": "https://github.com/ShvetankPrakash", "contributions": [ "doc" ] }, { - "login": "mmaz", - "name": "Mark Mazumder", - "avatar_url": "https://avatars.githubusercontent.com/mmaz", - "profile": "https://github.com/mmaz", + "login": "mpstewart1", + "name": "Matthew Stewart", + "avatar_url": "https://avatars.githubusercontent.com/mpstewart1", + "profile": "https://github.com/mpstewart1", "contributions": [ "doc" ] }, { - "login": "18jeffreyma", - "name": "Jeffrey Ma", - "avatar_url": "https://avatars.githubusercontent.com/18jeffreyma", - "profile": "https://github.com/18jeffreyma", + "login": "Naeemkh", + "name": "naeemkh", + "avatar_url": "https://avatars.githubusercontent.com/Naeemkh", + "profile": "https://github.com/Naeemkh", "contributions": [ "doc" ] }, { - "login": "uchendui", - "name": "Ikechukwu Uchendu", - "avatar_url": "https://avatars.githubusercontent.com/uchendui", - "profile": "https://github.com/uchendui", + "login": "profvjreddi", + "name": "Vijay Janapa Reddi", + "avatar_url": "https://avatars.githubusercontent.com/profvjreddi", + "profile": "https://github.com/profvjreddi", "contributions": [ "doc" ] }, { - "login": "jessicaquaye", - "name": "Jessica Quaye", - "avatar_url": "https://avatars.githubusercontent.com/jessicaquaye", - "profile": "https://github.com/jessicaquaye", + "login": "mmaz", + "name": "Mark Mazumder", + "avatar_url": "https://avatars.githubusercontent.com/mmaz", + "profile": "https://github.com/mmaz", "contributions": [ "doc" ] }, { - "login": "DivyaAmirtharaj", - "name": "Divya", - "avatar_url": "https://avatars.githubusercontent.com/DivyaAmirtharaj", - "profile": "https://github.com/DivyaAmirtharaj", + "login": "BaeHenryS", + "name": "Henry Bae", + "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", + "profile": "https://github.com/BaeHenryS", "contributions": [ "doc" ] }, { - "login": "mpstewart1", - "name": "Matthew Stewart", - "avatar_url": "https://avatars.githubusercontent.com/mpstewart1", - "profile": "https://github.com/mpstewart1", + "login": "oishib", + "name": "oishib", + "avatar_url": "https://avatars.githubusercontent.com/oishib", + "profile": "https://github.com/oishib", "contributions": [ "doc" ] }, { - "login": "Mjrovai", - "name": "Marcelo Rovai", - "avatar_url": "https://avatars.githubusercontent.com/Mjrovai", - "profile": "https://github.com/Mjrovai", + "login": "marcozennaro", + "name": "Marco Zennaro", + "avatar_url": "https://avatars.githubusercontent.com/marcozennaro", + "profile": "https://github.com/marcozennaro", "contributions": [ "doc" ] }, { - "login": "oishib", - "name": "oishib", - "avatar_url": "https://avatars.githubusercontent.com/oishib", - "profile": "https://github.com/oishib", + "login": "jessicaquaye", + "name": "Jessica Quaye", + "avatar_url": "https://avatars.githubusercontent.com/jessicaquaye", + "profile": "https://github.com/jessicaquaye", "contributions": [ "doc" ] }, { - "login": "profvjreddi", - "name": "Vijay Janapa Reddi", - "avatar_url": "https://avatars.githubusercontent.com/profvjreddi", - "profile": "https://github.com/profvjreddi", + "login": "sjohri20", + "name": "sjohri20", + "avatar_url": "https://avatars.githubusercontent.com/sjohri20", + "profile": "https://github.com/sjohri20", "contributions": [ "doc" ] @@ -151,28 +160,19 @@ ] }, { - "login": "marcozennaro", - "name": "Marco Zennaro", - "avatar_url": "https://avatars.githubusercontent.com/marcozennaro", - "profile": "https://github.com/marcozennaro", - "contributions": [ - "doc" - ] - }, - { - "login": "ishapira1", - "name": "ishapira", - "avatar_url": "https://avatars.githubusercontent.com/ishapira1", - "profile": "https://github.com/ishapira1", + "login": "jaysonzlin", + "name": "Jayson Lin", + "avatar_url": "https://avatars.githubusercontent.com/jaysonzlin", + "profile": "https://github.com/jaysonzlin", "contributions": [ "doc" ] }, { - "login": "sophiacho1", - "name": "sophiacho1", - "avatar_url": "https://avatars.githubusercontent.com/sophiacho1", - "profile": "https://github.com/sophiacho1", + "login": "DivyaAmirtharaj", + "name": "Divya", + "avatar_url": "https://avatars.githubusercontent.com/DivyaAmirtharaj", + "profile": "https://github.com/DivyaAmirtharaj", "contributions": [ "doc" ] diff --git a/README.md b/README.md index 3519e09c..9dec61d9 100644 --- a/README.md +++ b/README.md @@ -88,31 +88,31 @@ quarto render - + + - - - - - - - - - + + - - + - + + + - - + + + + + + +
      Shvetank Prakash
      Shvetank Prakash
      📖      		Marcelo Rovai
      Marcelo Rovai
      📖      		ishapira
      ishapira
      📖      		 aptl26
      aptl26
      📖      		sjohri20
      sjohri20
      📖      		Jayson Lin
      Jayson Lin
      📖      		Henry Bae
      Henry Bae
      📖
      naeemkh
      naeemkh
      📖      		Mark Mazumder
      Mark Mazumder
      📖      		 Jeffrey Ma
      Jeffrey Ma
      📖      		 Ikechukwu Uchendu
      Ikechukwu Uchendu
      📖      		Jessica Quaye
      Jessica Quaye
      📖
      Divya
      Divya
      📖      		sophiacho1
      sophiacho1
      📖      		Shvetank Prakash
      Shvetank Prakash
      📖      		 Matthew Stewart
      Matthew Stewart
      📖      		Marcelo Rovai
      Marcelo Rovai
      📖      		oishib
      oishib
      📖      		naeemkh
      naeemkh
      📖      		 Vijay Janapa Reddi
      Vijay Janapa Reddi
      📖
      Colby Banbury
      Colby Banbury
      📖      		Mark Mazumder
      Mark Mazumder
      📖      		Henry Bae
      Henry Bae
      📖      		oishib
      oishib
      📖      		 Marco Zennaro
      Marco Zennaro
      📖      		ishapira
      ishapira
      📖      		sophiacho1
      sophiacho1
      📖      		Jessica Quaye
      Jessica Quaye
      📖
      sjohri20
      sjohri20
      📖      		Colby Banbury
      Colby Banbury
      📖      		Jayson Lin
      Jayson Lin
      📖      		Divya
      Divya
      📖
      diff --git a/contributors.qmd b/contributors.qmd index 24df99e1..37d01313 100644 --- a/contributors.qmd +++ b/contributors.qmd @@ -8,31 +8,31 @@ We extend our sincere thanks to the diverse group of individuals who have genero - + + - - - - - - - - - + + - - + - + + + - - + + + + + + +
      Shvetank Prakash
      Shvetank Prakash
      📖      		Marcelo Rovai
      Marcelo Rovai
      📖      		ishapira
      ishapira
      📖      		 aptl26
      aptl26
      📖      		sjohri20
      sjohri20
      📖      		Jayson Lin
      Jayson Lin
      📖      		Henry Bae
      Henry Bae
      📖
      naeemkh
      naeemkh
      📖      		Mark Mazumder
      Mark Mazumder
      📖      		 Jeffrey Ma
      Jeffrey Ma
      📖      		 Ikechukwu Uchendu
      Ikechukwu Uchendu
      📖      		Jessica Quaye
      Jessica Quaye
      📖
      Divya
      Divya
      📖      		sophiacho1
      sophiacho1
      📖      		Shvetank Prakash
      Shvetank Prakash
      📖      		 Matthew Stewart
      Matthew Stewart
      📖      		Marcelo Rovai
      Marcelo Rovai
      📖      		oishib
      oishib
      📖      		naeemkh
      naeemkh
      📖      		 Vijay Janapa Reddi
      Vijay Janapa Reddi
      📖
      Colby Banbury
      Colby Banbury
      📖      		Mark Mazumder
      Mark Mazumder
      📖      		Henry Bae
      Henry Bae
      📖      		oishib
      oishib
      📖      		 Marco Zennaro
      Marco Zennaro
      📖      		ishapira
      ishapira
      📖      		sophiacho1
      sophiacho1
      📖      		Jessica Quaye
      Jessica Quaye
      📖
      sjohri20
      sjohri20
      📖      		Colby Banbury
      Colby Banbury
      📖      		Jayson Lin
      Jayson Lin
      📖      		Divya
      Divya
      📖

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