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Deep Learning on Edge Computing Devices
Design Challenges of Algorithm and Architecture
1st Edition - February 2, 2022
Authors: Xichuan Zhou, Haijun Liu, Cong Shi, Ji Liu
Paperback ISBN:9780323857833
9 7 8 - 0 - 3 2 3 - 8 5 7 8 3 - 3
eBook ISBN:9780323909273
9 7 8 - 0 - 3 2 3 - 9 0 9 2 7 - 3
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks.… Read more
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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
Computer scientists and researchers in applied informatics, Artificial Intelligence, data science, Cloud computing, networking, and information technology; Researchers in hardware design, deep learning, and optimization; Engineers working on Edge or embedded AI or deep learning applications
Cover image
Title page
Table of Contents
Copyright
Preface
Acknowledgements
Part 1: Introduction
Chapter 1: Introduction
Abstract
1.1. Background
1.2. Applications and trends
1.3. Concepts and taxonomy
1.4. Challenges and objectives
1.5. Outline of the book
References
Chapter 2: The basics of deep learning
Abstract
2.1. Feedforward neural networks
2.2. Deep neural networks
2.3. Learning objectives and training process
2.4. Computational complexity
References
Part 2: Model and algorithm
Chapter 3: Model design and compression
Abstract
3.1. Background and challenges
3.2. Design of lightweight neural networks
3.3. Model compression
References
Chapter 4: Mix-precision model encoding and quantization
Abstract
4.1. Background and challenges
4.2. Rate-distortion theory and sparse encoding
4.3. Bitwise bottleneck quantization methods
4.4. Application to efficient image classification
References
Chapter 5: Model encoding of binary neural networks
Abstract
5.1. Background and challenges
5.2. The basic of binary neural network
5.3. The cellular binary neural network with lateral connections
5.4. Application to efficient image classification
Chapter 7: Algorithm and hardware codesign of sparse binary network on-chip
Abstract
7.1. Background and challenges
7.2. Algorithm design and optimization
7.3. Near-memory computing architecture
7.4. Applications of deep adaptive network on chip
References
Chapter 8: Hardware architecture optimization for object tracking
Abstract
8.1. Background and challenges
8.2. Algorithm
8.3. Hardware implementation and optimization
8.4. Application experiments
References
Chapter 9: SensCamera: A learning-based smart camera prototype
Abstract
9.1. Challenges beyond pattern recognition
9.2. Compressive convolutional network model
9.3. Hardware implementation and optimization
9.4. Applications of SensCamera
References
Index
No. of pages: 198
Language: English
Published: February 2, 2022
Imprint: Elsevier
Paperback ISBN: 9780323857833
eBook ISBN: 9780323909273
XZ
Xichuan Zhou
Xichuan Zhou is Professor and Vice Dean in the School of Microelectronics and Communication Engineering, at Chongqing University, China. He received his PhD from Zhejiang University. His research focuses on embedded neural computing, brain-like sensing, and pervasive computing. He has won professional awards for his work, and has published over 50 papers.
Affiliations and expertise
Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China; Vice Dean, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
HL
Haijun Liu
Research Assistant, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China. He received his B.Eng, M.Eng and Ph.D degree from University of Electronic Science and Technology of China in 2011, 2014 and 2019, and has been a visiting scholar of Kyoto University from 2018 to 2019. His main research interests include manifold learning, metric learning, deep learning, subspace clustering and sparse representation in computer vision and machine learning, with focuses on human action detection and recognition, face detection and recognition, person detection and re-identification, remote sensing image processing, and medical image analysis.
Affiliations and expertise
Research Assistant, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
CS
Cong Shi
Cong Shi is a Research Professor in the School of Microelectronics and Communication Engineering, at Chongqing University, China. He received his PhD from Tsinghua University and has held the position of Postdoctoral Fellow with the Schepens Eye Research Institute, at Harvard Medical School. His research focuses on AI-based visual processing system-on-chips, and algorithm hardware co-design techniques. He has published over 30 papers.
Affiliations and expertise
Research Professor, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
JL
Ji Liu
Ji Liu is the Head of the AI platform department and the director of the Seattle AI lab for Kwai Inc. He has previously been a faculty member in computer science at the University of Rochester, USA. He received his PhD from the University of Wisconsin-Madison. His research includes machine learning, optimization, computer vision, reinforcement learning, and other areas. He has published over 100 papers.
Affiliations and expertise
Head, AI Platform Department, Seattle AI Lab, Kwai Inc., Seattle, Washington, United States of America; Director, Seattle AI Lab, Kwai Inc., Seattle, Washington, USA