Low-Power Computer Vision
Chapman & Hall/CRC (Verlag)
978-0-367-75528-7 (ISBN)
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.
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 InferenceAmir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael Mahoney, and Kurt Keutzer
Bibliography
Index
| Erscheinungsdatum | 07.02.2022 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Computer Vision |
| Zusatzinfo | 58 Tables, black and white; 61 Line drawings, color; 39 Line drawings, black and white; 1 Halftones, color; 62 Illustrations, color; 39 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 807 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Grafik / Design |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 0-367-75528-9 / 0367755289 |
| ISBN-13 | 978-0-367-75528-7 / 9780367755287 |
| Zustand | Neuware |
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