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

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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

This is a digital product.

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture is written by Xichuan Zhou; Haijun Liu; Cong Shi; Ji Liu and published by Elsevier (S&T). The Digital and eTextbook ISBNs for Deep Learning on Edge Computing Devices are 9780323909273, 0323909272 and the print ISBNs are 9780323857833, 0323857833.