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

Description

Product ID:	9780323857833
Product Form:	Paperback / softback
Country of Manufacture:	NL
Title:	Deep Learning on Edge Computing Devices
Subtitle:	Design Challenges of Algorithm and Architecture
Authors:	Author: Cong Shi, Xichuan Zhou, Ji Liu, Haijun Liu
Page Count:	198
Subjects:	Artificial intelligence, Artificial intelligence, Machine learning, Machine learning
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.
Imprint Name:	Elsevier - Health Sciences Division
Publisher Name:	Elsevier - Health Sciences Division
Country of Publication:	GB
Publishing Date:	2022-02-07

Weight	328 g
Dimensions	151 × 227 × 18 mm