Song Guo, Qihua Zhou

Machine Learning on Commodity Tiny Devices (eBook, PDF)

Theory and Practice

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 268
• Erscheinungstermin: 13. Dezember 2022
• Englisch
• ISBN-13: 9781000780352
• Artikelnr.: 66398702

Autorenporträt

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.

Inhaltsangabe

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