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

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