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.

• Focuses on hardware architecture and embedded deep learning, including neural networks
• Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications
• Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud
• Describes how to maximize the performance of deep learning on Edge-computing devices
• Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring

Book Details

• Authors: Xichuan Zhou, Haijun Liu, Cong Shi, Ji Liu
• Publisher: Elsevier (S&T)
• eBook ISBN: 9780323909273, 0323909272
• Print ISBN: 9780323857833, 0323857833