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- Дата: 12-11-2022, 07:01
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![Machine Learning on Commodity Tiny Devices: Theory and Practice](/uploads/posts/2022-11/1668225706_2871_machin__l_arning_on_commodity_tiny_d_vic_s.jpg)
Автор: Song Guo, Qihua Zhou
Издательство: CRC Press
Год: 2023
Страниц: 268
Язык: английский
Формат: pdf (true)
Размер: 30.3 MB
This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It 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.