Xichuan Zhou (Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China; Vice Dean, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China): Deep Learning on Edge Computing Devices
Xichuan Zhou (Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China; Vice Dean, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China)
, Haijun Liu (Research Assistant, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China)
, Cong Shi (Research Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China)
, Ji Liu (Head, AI Platform Department, Seattle AI Lab, Kwai Inc., Seattle, Washington, United States of America; Director, Seattle AI Lab, Kwai Inc., Seattle, Washington, USA)
Deep Learning on Edge Computing Devices
Buch
- Design Challenges of Algorithm and Architecture
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- Elsevier - Health Sciences Division, 02/2022
- Einband: Kartoniert / Broschiert
- Sprache: Englisch
- ISBN-13: 9780323857833
- Umfang: 198 Seiten
- Sonstiges: 35 illustrations (15 in full color); Illustrations, unspecified
- Gewicht: 328 g
- Maße: 227 x 151 mm
- Stärke: 18 mm
- Erscheinungstermin: 18.2.2022
Achtung: Artikel ist nicht in deutscher Sprache!
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Klappentext
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 book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design.