Deep Learning on Edge Computing Devices
Design Challenges of Algorithm and Architecture
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the…
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Produktdetails
Weitere Autoren: Liu, Haijun (Research Assistant, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China) / Shi, Cong (Research Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China) / Liu, Ji (Head, AI Platform Department, Seattle AI Lab, Kwai Inc., Seattle, Washington, United States of America; Director, Seattle AI Lab, Kwai Inc., Seattle, Washington, USA)
- ISBN: 978-0-323-85783-3
- EAN: 9780323857833
- Produktnummer: 38501722
- Verlag: Elsevier - Health Sciences Division
- Sprache: Englisch
- Erscheinungsjahr: 2022
- Seitenangabe: 198 S.
- Masse: H15.1 cm x B22.7 cm x D1.8 cm 328 g
- Gewicht: 328
- Sonstiges: Professional & Vocational
Über den Autor
Xichuan Zhou is Professor and Vice Dean in the School of Microelectronics and Communication Engineering, at Chongqing University, China. He received his PhD from Zhejiang University. His research focuses on embedded neural computing, brain-like sensing, and pervasive computing. He has won professional awards for his work, and has published over 50 papers.
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