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, presenting neural network algorithms and design optimization approaches for Edge-deep learning. It covers core concepts, theories, and algorithms and architecture optimization, with a comprehensive example of smart surveillance cameras.

Format: Paperback / softback
Length: 198 pages
Publication date: 07 February 2022
Publisher: Elsevier - Health Sciences Division

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture delves into the realm of hardware architecture and embedded deep learning, encompassing neural networks. The title serves as a valuable resource for researchers seeking to optimize the performance of Edge-deep learning models for mobile computing and various other applications. By presenting neural network algorithms and hardware design optimization approaches, the book empowers researchers to maximize the potential of Edge-deep learning models.

Applications are introduced in each section, providing a comprehensive overview of the field. A notable example, smart surveillance cameras, is presented at the end of the book, showcasing innovation in both algorithm and hardware architecture. The book is structured into three parts: core concepts, theories, and algorithms and architecture optimization.

This comprehensive text offers a solution for researchers seeking to enhance the efficiency of deep learning models on Edge-computing devices through collaborative design of algorithms and hardware. It serves as a valuable resource for scholars, researchers, and practitioners in the field of deep learning and edge computing.

Weight: 450g
Dimension: 229 x 152 (mm)
ISBN-13: 9780323857833