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Machine Learning on Commodity Tiny Devices de Qihua Zhou

Machine Learning on Commodity Tiny Devices

Autor Qihua Zhou, Song Guo
en Limba Engleză Paperback – 19 dec 2024
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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  Taylor & Francis Ltd. – 19 dec 2024 35676 lei  6-8 săpt.
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Specificații

ISBN-13: 9781032374260
ISBN-10: 1032374268
Pagini: 250
Dimensiuni: 178 x 254 mm
Greutate: 0.47 kg
Editura: Taylor & Francis Ltd.

Cuprins

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

Notă biografică

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.