Deep Learning on Edge Computing Devices : Design Challenges of Algorithm and Architecture Paperback / softback
by Xichuan (Professor, School of Microelectronics and Communication Engineering, Chongqing Univer Zhou, Haijun (Research Assistant, School of Microelectronics and Communication Engineering, Chongqing Liu, Cong (Research Professor, School of Microelectronics and Communication Engineering, Chongqing U Shi, Ji (Head, AI Platform Department, Seattle AI Lab, Kwai Inc., Seattle, Washington, United States Liu
Paperback / softback
Description
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.
Information
-
Only a few left - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:198 pages, 35 illustrations (15 in full color); Illustrations, unspecified
- Publisher:Elsevier - Health Sciences Division
- Publication Date:07/02/2022
- Category:
- ISBN:9780323857833
Information
-
Only a few left - usually despatched within 24 hours
- Format:Paperback / softback
- Pages:198 pages, 35 illustrations (15 in full color); Illustrations, unspecified
- Publisher:Elsevier - Health Sciences Division
- Publication Date:07/02/2022
- Category:
- ISBN:9780323857833