Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture (Paperback) (ISBN-13: 9780323857833)

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.

Author: Xichuan Zhou, Haijun Liu, Cong Shi
ISBN-10: 0323857833
ISBN-13: 9780323857833
Publisher: Elsevier
Language: English
Published: 02/07/2022
Pages: 198
Format: Paperback
Weight: 0.60lbs
Size: 9.00h x 6.00w x 0.42d