Machine Learning on Commodity Tiny Devices: Theory and Practice

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.

本書旨在探討微型機器學習（TinyML）軟硬體協同運作在邊緣智能應用中的應用。本書介紹了設備上的學習技術，包括模型級神經網絡設計、算法級訓練優化和硬體級指令加速。

分析傳統雲端計算的限制將揭示出設備上的學習是滿足邊緣智能應用需求的一個有前景的研究方向。對於微型機器學習的尖端研究，實現高效的學習框架並實現系統級加速是其中最基本的問題之一。本書全面討論了最新的研究進展，並提供了關於設計微型機器學習框架的系統級見解，包括神經網絡設計、訓練算法優化和特定領域的硬體加速。它確定了在現實世界中部署微型機器學習任務時的主要挑戰，並指導研究人員部署可靠的學習系統。

本書將對邊緣智能領域的學生和學者感興趣，特別是那些具備足夠專業的邊緣人工智能技能的人。對於研究人員實現高性能微型機器學習系統，本書也將是一個優秀的指南。

Song Guo is a Full Professor leading the Edge Intelligence Lab and Research Group of Networking and Mobile Computing at the Hong Kong Polytechnic University. Professor Guo is a Fellow of the Canadian Academy of Engineering, Fellow of the IEEE, Fellow of the AAIA, and Clarivate Highly Cited Researcher.

Qihua Zhou is a Ph.D. student with the Department of Computing, at the Hong Kong Polytechnic University. His research interests include distributed AI systems, large-scale parallel processing, tiny ML systems and domain-specific accelerators.

宋國是香港理工大學網絡與移動計算系的邊緣智能實驗室和研究小組的全職教授。宋教授是加拿大工程院院士、IEEE院士、AAIA院士和Clarivate高被引研究者。

周啟華是香港理工大學計算系的博士生。他的研究興趣包括分佈式人工智能系統、大規模並行處理、微型機器學習系統和特定領域加速器。