Embedded Machine Learning for Cyber-Physical by Sudeep Pasricha (.PDF)

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Hardware Architectures by Sudeep Pasricha, Muhammad Shafique
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Overview: This book presents recent advances towards the goal of enabling efficient implementation of Machine Learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying Machine Learning to innovative application domains, exploring the efficient hardware design of efficient Machine Learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.

Machine Learning (ML) has emerged as a prominent approach for achieving state-of-the-art accuracy for many data analytic applications, ranging from computer vision (e.g., classification, segmentation, and object detection in images and video), speech recognition, language translation, healthcare diagnostics, robotics, and autonomous vehicles to business and financial analysis. The driving force of the ML success is the advent of neural network (NN) algorithms, such as deep neural networks (DNNs)/Deep Learning (DL) and spiking neural networks (SNNs) with support from today’s evolving computing landscape to better exploit data and thread-level parallelism with ML accelerators.

This volume of the book focuses on addressing these challenges from a hardware perspective, with multiple solutions towards the design of efficient accelerators, memory, and emerging technology substrates for embedded ML systems.
Genre: Non-Fiction > Tech & Devices

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