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Machine Learning on Commodity Tiny Devices - Theory and Practice (Hardcover)
Loot Price: R2,165
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Machine Learning on Commodity Tiny Devices - Theory and Practice (Hardcover)
Expected to ship within 9 - 15 working days
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This book aims at the tiny machine learning (TinyML) software and
hardware synergy for edge intelligence applications. This book
presents on-device learning techniques covering model-level neural
network design, algorithm-level training optimization and
hardware-level instruction acceleration. Analyzing the limitations
of conventional in-cloud computing would reveal that on-device
learning is a promising research direction to meet the requirements
of edge intelligence applications. As to the cutting-edge research
of TinyML, implementing a high-efficiency learning framework and
enabling system-level acceleration is one of the most fundamental
issues. This book presents a comprehensive discussion of the latest
research progress and provides system-level insights on designing
TinyML frameworks, including neural network design, training
algorithm optimization and domain-specific hardware acceleration.
It identifies the main challenges when deploying TinyML tasks in
the real world and guides the researchers to deploy a reliable
learning system. This book will be of interest to students and
scholars in the field of edge intelligence, especially to those
with sufficient professional Edge AI skills. It will also be an
excellent guide for researchers to implement high-performance
TinyML systems.
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