Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture 1st Edition Xichuan Zhou

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture – Ebook Instant Download/Delivery ISBN(s): 9780323857833,0323857833

Product details:

• ISBN-10 ‏ : ‎ 0323857833
• ISBN-13 ‏ : ‎ 978-0323857833
• Author(s): Xichuan Zhou, Haijun Liu, Cong Shi, Ji Liu

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.

Table contents:

Part 1: Introduction
Chapter 1: Introduction

Chapter 2: The basics of deep learning

Part 2: Model and algorithm
Chapter 3: Model design and compression

Chapter 4: Mix-precision model encoding and quantization

Chapter 5: Model encoding of binary neural networks

Part 3: Architecture optimization
Chapter 6: Binary neural network computing architecture

Chapter 7: Algorithm and hardware codesign of sparse binary network on-chip

Chapter 8: Hardware architecture optimization for object tracking

Chapter 9: SensCamera: A learning-based smart camera prototype

References
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