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Machine Learning on Commodity Tiny Devices

- Theory and Practice
Af: Song Guo, Qihua Zhou Engelsk Hardback

Machine Learning on Commodity Tiny Devices

- Theory and Practice
Af: Song Guo, Qihua Zhou Engelsk Hardback
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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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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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Produktdetaljer
Sprog: Engelsk
Sider: 250
ISBN-13: 9781032374239
Indbinding: Hardback
Udgave:
ISBN-10: 1032374233
Kategori: Automatisk styrings- & reguleringsteknik
Udg. Dato: 13 dec 2022
Længde: 22mm
Bredde: 260mm
Højde: 185mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 13 dec 2022
Forfatter(e): Song Guo, Qihua Zhou
Forfatter(e) Song Guo, Qihua Zhou

Kategori Automatisk styrings- & reguleringsteknik

ISBN-13 9781032374239

Sprog Engelsk

Indbinding Hardback

Sider 250

Udgave

Længde 22mm

Bredde 260mm

Højde 185mm

Udg. Dato 13 dec 2022

Oplagsdato 13 dec 2022

Forlag Taylor & Francis Ltd