Low-Power Computer Vision Improve the Efficiency of Artificial Intelligence

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.

Section I Introduction Book IntroductionYung-Hsiang Lu, George K. Thiruvathukal, Jaeyoun Kim, Yiran Chen, and Bo Chen History of Low-Power Computer Vision ChallengeYung-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 VisionAbhinav 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 inferenceYu 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 SearchXin Xia, Xuefeng Xiao, and Xing Wang Fast Adjustable Threshold For Uniform Neural Network QuantizationAlexander 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 NetworksMarios 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 InferenceAmir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael Mahoney, and Kurt Keutzer Bibliography Index

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, softwareengineering, 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 neuromorphiccomputing, 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.