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Machine Learning on Commodity Tiny Devices: Theory and Practice

Machine Learning on Commodity Tiny Devices: Theory and Practice

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Availability:In StockContributor:Song Guo, Qihua ZhouPublish date:2022-12-13Pages:250
Language:EnglishPublisher:CRC PressISBN-13:9781032374239ISBN-10:1032374233UPC:9781032374239Book Category:ComputersBook Subcategory:Data Science, Artificial Intelligence, ProgrammingBook Topic:Machine Learning, GamesSize:10.00 x 7.00 x 0.63 inchesWeight:1.5013Product ID:SC5NA8KJNF

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.

Language:EnglishPublisher:CRC PressISBN-13:9781032374239ISBN-10:1032374233UPC:9781032374239Book Category:ComputersBook Subcategory:Data Science, Artificial Intelligence, ProgrammingBook Topic:Machine Learning, GamesSize:10.00 x 7.00 x 0.63 inchesWeight:1.5013Product ID:SC5NA8KJNF

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.


Publisher: CRC Press

Contributor(s)

Song Guo, Qihua Zhou

Author

Song Guo, Qihua Zhou

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