Low-Power Computer Vision : Improve the Efficiency of Artificial Intelligence (Hardcover)

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