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portada Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture
Type
Physical Book
Publisher
Author
Language
English
Pages
198
Format
Paperback
ISBN13
9780323857833
Edition No.
1

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture

Xichuan Zhou; Haijun Liu; Cong Shi; Ji Liu (Author) · Elsevier · Paperback

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture - Xichuan Zhou; Haijun Liu; Cong Shi; Ji Liu

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Synopsis "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 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.

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