Fixed broken reference

efficient_ai.qmd

@@ -57,7 +57,7 @@ Model compression methods are very important for bringing deep learning models t
 
 **NN Accelerators**: Fixed function neural network accelerators are hardware accelerators designed explicitly for neural network computations. These can be standalone chips or part of a larger system-on-chip (SoC) solution. By optimizing the hardware for the specific operations that neural networks require, such as matrix multiplications and convolutions, NN accelerators can achieve faster inference times and lower power consumption compared to general-purpose CPUs and GPUs. They are especially beneficial in TinyML devices with power or thermal constraints, such as smartwatches, micro-drones, or robotics.
 
-But these are all but the most common place examples, there are a number of other types of hardware that are emerging that have the potential to offer signficiant advantages for inference. These include but are not limited to neuromorphic hardware, photonic computing, and so forth. In [@sec-ai_hw] we will explore these in greater detail.
+But these are all but the most common place examples, there are a number of other types of hardware that are emerging that have the potential to offer signficiant advantages for inference. These include but are not limited to neuromorphic hardware, photonic computing, and so forth. In [@sec-aihw] we will explore these in greater detail.
 
 Efficient hardware for inference not only speeds up the process but also saves energy, extends battery life, and can operate in real-time conditions. As AI continues to be integrated into a myriad of applications – from smart cameras to voice assistants – the role of optimized hardware will only become more prominent. By leveraging these specialized hardware components, developers and engineers can bring the power of AI to devices and situations that were previously unthinkable.
 

hw_acceleration.qmd

@@ -1,4 +1,4 @@
-# AI Acceleration
+# AI Acceleration 
 
 ::: {.callout-note collapse="true"}
 ## Learning Objectives
@@ -19,7 +19,7 @@ Explanation: Here, readers are provided with a foundational understanding of the
 - The Need for Hardware Acceleration
 - General Principles of Hardware Acceleration
 
-## Types of Hardware Accelerators
+## Types of Hardware Accelerators {#sec-aihw}
 
 Explanation: This section offers an overview of the hardware options available for accelerating AI tasks, discussing each type in detail, and comparing their advantages and disadvantages. It is key for readers to comprehend the various hardware solutions available for specific AI tasks, and to make informed decisions when selecting hardware solutions.