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

Author

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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ISBN-10

0323857833

ISBN-13

978-0323857833

Publisher

Elsevier

Price

151.3

File Type

PDF

Page No.

198

Xichuan Zhou

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Helps users maximize the performance of deep learning on Edge-computing devices

From the Back Cover

Deep learning models deployed on Edge devices, such as mobile phones and IoT terminals, generally use Cloud computing, presenting a range of concerns around privacy, latency and power consumption. In turn, Edge computing enables inference operations, and even training progress, to be completed on embedded devices themselves, rather than in the Cloud. With on-device deep learning, reliability becomes independent of network availability or bandwidth, data processing becomes much faster, and the problems associated with the Cloud are eliminated. 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. This book presents a summary of technology around Edge-deep learning. Structured into three parts, the first introduces core concepts; the second presents theories and algorithms; and part three details 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.

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

Artificial Intelligence

Deep Learning

Cloud Computing

Xichuan Zhou

Elsevier

Xichuan Zhou

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