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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.
Fundamentals: On-Device Learning Paradigm
Preliminary: Theories and Algorithms
Model-Level Design: Computation Acceleration and Communication Saving
Hardware-Level Design: Neural Engines and Tensor Accelerators
Infrastructure-Level Design: Serverless and Decentralized Machine Learning
System-Level Design: From Standalone to Clusters
Application: Image-Based Visual Perception
Application: Video-Based Real-Time Processing
Application: Privacy, Security, Robustness and Trustworthiness in Edge AI