Deep Learning on Edge Computing Devices

Design Challenges of Algorithm and Architecture

Ji Liu author Xichuan Zhou author Haijun Liu author Cong Shi author

Format:Paperback

Publisher:Elsevier - Health Sciences Division

Published:7th Feb '22

Should be back in stock very soon

Deep Learning on Edge Computing Devices cover

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.

ISBN: 9780323857833

Dimensions: unknown

Weight: 410g

198 pages