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docs/contents/frameworks/frameworks.html

@@ -1647,7 +1647,7 @@ <h3 data-number="6.8.3" class="anchored" data-anchor-id="library"><span class="h
 <section id="choosing-the-right-framework" class="level2" data-number="6.9">
 <h2 data-number="6.9" class="anchored" data-anchor-id="choosing-the-right-framework"><span class="header-section-number">6.9</span> Choosing the Right Framework</h2>
 <p>Choosing the right machine learning framework for a given application requires carefully evaluating models, hardware, and software considerations. By analyzing these three aspects—models, hardware, and software—ML engineers can select the optimal framework and customize it as needed for efficient and performant on-device ML applications. The goal is to balance model complexity, hardware limitations, and software integration to design a tailored ML pipeline for embedded and edge devices.</p>
-<div id="fig-tf-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - General">
+<div id="fig-tf-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - General" data-align="center">
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@@ -1663,7 +1663,7 @@ <h3 data-number="6.9.1" class="anchored" data-anchor-id="model"><span class="hea
 </section>
 <section id="software" class="level3" data-number="6.9.2">
 <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="header-section-number">6.9.2</span> Software</h3>
-<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Model">
+<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Model" data-align="center">
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@@ -1677,7 +1677,7 @@ <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="
 </section>
 <section id="hardware" class="level3" data-number="6.9.3">
 <h3 data-number="6.9.3" class="anchored" data-anchor-id="hardware"><span class="header-section-number">6.9.3</span> Hardware</h3>
-<div id="fig-tf-hw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware">
+<div id="fig-tf-hw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Hardware" data-align="center">
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@@ -1723,7 +1723,7 @@ <h2 data-number="6.10" class="anchored" data-anchor-id="future-trends-in-ml-fram
 <section id="decomposition" class="level3" data-number="6.10.1">
 <h3 data-number="6.10.1" class="anchored" data-anchor-id="decomposition"><span class="header-section-number">6.10.1</span> Decomposition</h3>
 <p>Currently, the ML system stack consists of four abstractions as shown in <a href="#fig-mlsys-stack" class="quarto-xref">Figure&nbsp;<span>6.11</span></a>, namely (1) computational graphs, (2) tensor programs, (3) libraries and runtimes, and (4) hardware primitives.</p>
-<div id="fig-mlsys-stack" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="Four Abstractions in Current ML System Stack">
+<div id="fig-mlsys-stack" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="Four Abstractions in Current ML System Stack" data-align="center">
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 <img src="images/png/image8.png" class="img-fluid figure-img" data-align="center" data-caption="Four Abstractions in Current ML System Stack">

docs/contents/hw_acceleration/hw_acceleration.html

@@ -2006,8 +2006,8 @@ <h3 data-number="10.9.3" class="anchored" data-anchor-id="ml-for-efficient-hardw
 <p>A key goal is designing hardware architectures optimized for performance, power, and efficiency. ML introduces new techniques to automate and improve architecture design space exploration for general-purpose and specialized hardware like ML accelerators. Some promising examples include:</p>
 <ul>
 <li><strong>Architecture search for hardware:</strong> Search techniques like evolutionary algorithms <span class="citation" data-cites="kao2020gamma">(<a href="../../references.html#ref-kao2020gamma" role="doc-biblioref">Kao and Krishna 2020</a>)</span>, Bayesian optimization (<span class="citation" data-cites="reagen2017case">Reagen et al. (<a href="../../references.html#ref-reagen2017case" role="doc-biblioref">2017</a>)</span>, <span class="citation" data-cites="bhardwaj2020comprehensive">Bhardwaj et al. (<a href="../../references.html#ref-bhardwaj2020comprehensive" role="doc-biblioref">2020</a>)</span>), reinforcement learning (<span class="citation" data-cites="kao2020confuciux">Kao, Jeong, and Krishna (<a href="../../references.html#ref-kao2020confuciux" role="doc-biblioref">2020</a>)</span>, <span class="citation" data-cites="krishnan2022multiagent">Krishnan et al. (<a href="../../references.html#ref-krishnan2022multiagent" role="doc-biblioref">2022</a>)</span>) can automatically generate novel hardware architectures by mutating and mixing design attributes like cache size, number of parallel units, memory bandwidth, and so on. This allows for efficient navigation of large design spaces.</li>
-<li><strong>Predictive modeling for optimization:</strong> - ML models can be trained to predict hardware performance, power, and efficiency metrics for a given architecture. These become “surrogate models” <span class="citation" data-cites="krishnan2023archgym">(<a href="../../references.html#ref-krishnan2023archgym" role="doc-biblioref">Krishnan et al. 2023</a>)</span> for fast optimization and space exploration by substituting lengthy simulations.</li>
-<li><strong>Specialized accelerator optimization:</strong> - For specialized chips like tensor processing units for AI, automated architecture search techniques based on ML algorithms <span class="citation" data-cites="zhang2022fullstack">(<a href="../../references.html#ref-zhang2022fullstack" role="doc-biblioref">D. Zhang et al. 2022</a>)</span> show promise for finding fast, efficient designs.</li>
+<li><strong>Predictive modeling for optimization:</strong> ML models can be trained to predict hardware performance, power, and efficiency metrics for a given architecture. These become “surrogate models” <span class="citation" data-cites="krishnan2023archgym">(<a href="../../references.html#ref-krishnan2023archgym" role="doc-biblioref">Krishnan et al. 2023</a>)</span> for fast optimization and space exploration by substituting lengthy simulations.</li>
+<li><strong>Specialized accelerator optimization:</strong> For specialized chips like tensor processing units for AI, automated architecture search techniques based on ML algorithms <span class="citation" data-cites="zhang2022fullstack">(<a href="../../references.html#ref-zhang2022fullstack" role="doc-biblioref">D. Zhang et al. 2022</a>)</span> show promise for finding fast, efficient designs.</li>
 </ul>
 <div class="no-row-height column-margin column-container"><div id="ref-kao2020gamma" class="csl-entry" role="listitem">
 Kao, Sheng-Chun, and Tushar Krishna. 2020. <span>“Gamma: Automating the HW Mapping of DNN Models on Accelerators via Genetic Algorithm.”</span> In <em>Proceedings of the 39th International Conference on Computer-Aided Design</em>, 1–9. ACM. <a href="https://doi.org/10.1145/3400302.3415639">https://doi.org/10.1145/3400302.3415639</a>.
@@ -2031,7 +2031,7 @@ <h3 data-number="10.9.4" class="anchored" data-anchor-id="ml-to-optimize-manufac
 <li><strong>Process optimization:</strong> Supervised learning models can be trained on process data to identify factors that lead to low yields. The models can then optimize parameters to improve yields, throughput, or consistency.</li>
 <li><strong>Yield prediction:</strong> By analyzing test data from fabricated designs using techniques like regression trees, ML models can predict yields early in production, allowing process adjustments.</li>
 <li><strong>Defect detection:</strong> Computer vision ML techniques can be applied to images of designs to identify defects invisible to the human eye. This enables precision quality control and root cause analysis.</li>
-<li><strong>Proactive failure analysis:</strong> - ML models can help predict, diagnose, and prevent issues that lead to downstream defects and failures by analyzing structured and unstructured process data.</li>
+<li><strong>Proactive failure analysis:</strong> ML models can help predict, diagnose, and prevent issues that lead to downstream defects and failures by analyzing structured and unstructured process data.</li>
 </ul>
 <p>Applying ML to manufacturing enables process optimization, real-time quality control, predictive maintenance, and higher yields. Challenges include managing complex manufacturing data and variations. But ML is poised to transform semiconductor manufacturing.</p>
 </section>