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.

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 PhD student with the Department of Computing at the Hong Kong Polytechnic University. His research interests include distributed AI systems, large-scale parallel processing, TinyML systems and domain-specific accelerators.

1. Introduction 2. Fundamentals: On-device Learning Paradigm 3. Preliminary: Theories and Algorithms 4. Model-level Design: Computation Acceleration and Communication Saving 5. Hardware-level Design: Neural Engines and Tensor Accelerators 6. Infrastructure-level Design: Serverless and Decentralized Machine Learning 7. System-level Design: from Standalone to Clusters 8. Application: Image-based Visual Perception 9. Application: Video-based Real-time Processing 10. Application: Privacy, Security, Robustness and Trustworthiness in Edge AI