Low-power computer vision : improve the efficiency of artificial intelligence - Thiruvathukal, George K. (George Kuriakose) ([Place of publication not identified] : Chapman and Hall/CRC, 2022)
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 IntroductionBook IntroductionYung-Hsiang Lu, George K. Thiruvathukal, Jaeyoun Kim, Yiran Chen, and Bo ChenHistory of Low-Power Computer Vision ChallengeYung-Hsiang Lu and Xiao Hu, Yiran Chen, Joe Spisak, Gaurav Aggarwal, Mike Zheng Shou, and George K. ThiruvathukalSurvey on Energy-Efficient Deep Neural Networks for Computer VisionAbhinav Goel, Caleb Tung, Xiao Hu, Haobo Wang, and Yung-Hsiang Lu and George K. ThiruvathukalSection II Competition WinnersHardware 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 ZhaoProgressive Automatic Design of Search Space for One-Shot Neural Architecture SearchXin Xia, Xuefeng Xiao, and Xing WangFast Adjustable Threshold For Uniform Neural Network QuantizationAlexander Goncharenko, Andrey Denisov, and Sergey AlyamkinPower-efficient Neural Network Scheduling on Heterogeneous SoCsYing Wang, Xuyi Cai, and Xiandong ZhaoEfficient Neural Network ArchitecturesHan Cai and Song HanDesign Methodology for Low Power Image Recognition SystemsSoonhoi Ha, EunJin Jeong, Duseok Kang, Jangryul Kim, and Donghyun KangGuided Design for Efficient On-device Object Detection ModelTao Sheng and Yang LiuSection III Invited ArticlesQuantizing Neural NetworksMarios Fournarakis, Markus Nagel, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Tijmen BlankevoortA practical guide to designing efficient mobile architecturesMark Sandler and Andrew HowardA Survey of Quantization Methods for Efficient Neural Network InferenceAmir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael Mahoney, and Kurt KeutzerBibliographyIndex
OCLC-licensed vendor bibliographic record.
Keywords: COMPUTERS / Computer Graphics / General; COMPUTERS / Computer Graphics / Game Programming & Design; COMPUTERS / Computer Vision & Pattern Recognition; Computer vision; Low voltage systems