Low-Power Computer Vision (eBook, PDF)

Improve the Efficiency of Artificial Intelligence
Redaktion: Thiruvathukal, George K.; Chen, Bo; Chen, Yiran; Kim, Jaeyoun; Lu, Yung-Hsiang

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 438
• Erscheinungstermin: 22. Februar 2022
• Englisch
• ISBN-13: 9781000540925
• Artikelnr.: 63285471

Autorenporträt

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.

Inhaltsangabe

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 SoCsYing Wang
Xuyi Cai
and Xiandong Zhao Efficient Neural Network ArchitecturesHan Cai and Song Han Design Methodology for Low Power Image Recognition SystemsSoonhoi Ha
EunJin Jeong
Duseok Kang
Jangryul Kim
and Donghyun Kang Guided Design for Efficient On-device Object Detection ModelTao 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 architecturesMark 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