Low-Power Computer Vision

Improve the Efficiency of Artificial Intelligence
1. Auflage 2024
ISBN: 978-0-367-75528-7
Verlag: CRC Press

Buch, Englisch, 438 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 608 g

Reihe: Chapman & Hall/CRC Computer Vision

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.

Zielgruppe

Academic, Postgraduate, and Professional

Autoren/Hrsg.

Thiruvathukal, George K

Lu, Yung-Hsiang

Kim, Jaeyoun

Chen, Yiran

Chen, Bo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Handheld Programmierung
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik

Inhaltsverzeichnis

Section I Introduction

Book Introduction
Yung-Hsiang Lu, George K. Thiruvathukal, Jaeyoun Kim, Yiran Chen, and Bo Chen

History of Low-Power Computer Vision Challenge
Yung-Hsiang Lu and Xiao Hu, Yiran Chen, Joe Spisak, Gaurav Aggarwal, Mike Zheng Shou, and George K. Thiruvathukal

Survey on Energy-Efficient Deep Neural Networks for Computer Vision
Abhinav Goel, Caleb Tung, Xiao Hu, Haobo Wang, and Yung-Hsiang Lu and George K. Thiruvathukal

Section II Competition Winners

Hardware design and software practices for efficient neural network inference
Yu Wang, Xuefei Ning, Shulin Zeng, Yi Kai, Kaiyuan Guo, and Hanbo Sun, Changcheng Tang, Tianyi Lu, Shuang Liang, and Tianchen Zhao

Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
Xin Xia, Xuefeng Xiao, and Xing Wang

Fast Adjustable Threshold For Uniform Neural Network Quantization
Alexander Goncharenko, Andrey Denisov, and Sergey Alyamkin

Power-efficient Neural Network Scheduling on Heterogeneous SoCs
Ying Wang, Xuyi Cai, and Xiandong Zhao

Efficient Neural Network Architectures
Han Cai and Song Han

Design Methodology for Low Power Image Recognition Systems
Soonhoi Ha, EunJin Jeong, Duseok Kang, Jangryul Kim, and Donghyun Kang

Guided Design for Efficient On-device Object Detection Model
Tao Sheng and Yang Liu

Section III Invited Articles

Quantizing Neural Networks
Marios Fournarakis, Markus Nagel, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Tijmen Blankevoort

A practical guide to designing efficient mobile architectures
Mark Sandler and Andrew Howard

A Survey of Quantization Methods for Efficient Neural Network Inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael Mahoney, and Kurt Keutzer

Bibliography

Index

Über Autor(innen)

George K. Thiruvathukal is a professor of Computer Science at Loyola University Chicago, Illinois, USA. He is also a visiting faculty at Argonne National Laboratory. His research areas include high performance and distributed computing, software
engineering, and programming languages.

Yung-Hsiang Lu is a professor of Electrical and Computer Engineering at Purdue University, Indiana, USA. He is the first director of Purdue’s John Martinson Engineering Entrepreneurial Center. He is a fellow of the IEEE and distinguished scientist of the ACM. His research interests include computer vision, mobile systems, and cloud computing.

Jaeyoun Kim is a technical program manager at Google, California, USA. He leads AI research projects, including MobileNets and TensorFlow Model Garden, to build state-of-the-art machine learning models and modeling libraries for computer vision and natural language processing.

Yiran Chen is a professor of Electrical and Computer Engineering at Duke University, North Carolina, USA. He is a fellow of the ACM and the IEEE. His research areas include new memory and storage systems, machine learning and neuromorphic
computing, and mobile computing systems.

Bo Chen is the Director of AutoML at DJI, Guangdong, China. Before joining DJI, he was a researcher at Google, California, USA. His research interests are the optimization of neural network software and hardware as well as landing AI technology in products with stringent resource constraints.