Machine Learning on Commodity Tiny Devices: Theory and Practice [Hardcover]

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

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.

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 learnil3¨