Deep Learning on Edge Computing Devices

Design Challenges of Algorithm and Architecture
Erscheinungsjahr 2022
ISBN: 978-0-323-85783-3
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 198 Seiten, Format (B × H): 227 mm x 151 mm, Gewicht: 328 g

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.

Zielgruppe

<p>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.</p>

Autoren/Hrsg.

Shi, Cong

Zhou, Xichuan

Liu, Haijun

Liu, Ji

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing

Inhaltsverzeichnis

Part 1. Introduction
1. Introduction

Part 2. Theory and Algorithm
2. Model Inference on Edge Device
3. Model Training on Edge Device
4. Network Encoding and Quantization

Part 3. Architecture Optimization
5. DANoC: An Algorithm and Hardware Codesign Prototype
6. Ensemble Spiking Networks on Edge Device
7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype

Über Autor(innen)

Shi, Cong
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.

Zhou, Xichuan
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.

Liu, Haijun
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.

Liu, Ji
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.