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Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence
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eBay-Artikelnr.:403941007790
Artikelmerkmale
- Artikelzustand
- Book Title
- Low-Power Computer Vision: Improve the Efficiency of Artificial I
- Publication Date
- 2022-02-23
- Pages
- 436
- ISBN
- 9780367744700
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
0367744708
ISBN-13
9780367744700
eBay Product ID (ePID)
15057244939
Product Key Features
Number of Pages
436 Pages
Publication Name
Low-Power Computer Vision
Language
English
Subject
Programming / Games, Computer Graphics, Engineering (General), Computer Vision & Pattern Recognition
Publication Year
2022
Type
Textbook
Subject Area
Computers, Technology & Engineering
Series
Chapman and Hall/Crc Computer Vision Ser.
Format
Hardcover
Dimensions
Item Weight
29.7 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
College Audience
LCCN
2021-042753
Dewey Edition
23
Reviews
On device AI has become increasingly important for reasons of latency, privacy and overall autonomy as computing becomes more and more ambient. Moreover, making AI, in particular computer vision, efficient and run well in low resource computing environments using frameworks like PyTorch is a priority of the industry to enable this. The IEEE Low-Power Computer Vision Challenge is one such effort that has and continues to push the field forward allowing us to make progress in this area. Facebook has been a proud sponsor and supporter of this challenge since 2018 and this book presents the winners' solutions from previous challenges and can guide researchers, engineers, and students to design efficient on device AI. -- Joe Spisak, Product Lead at Facebook Artificial Intelligence Computer vision is at the center of recent breakthroughs in artificial intelligence. Being able to process visual data in low-power computing environments will enable great advances in the field in areas such as edge computing and Internet of Things. This book presents work by experts in the field and their winning solutions. It is an indispensable resource for anyone interested creating AI technologies in resource constrained computing environments -- Mark Liao, Director, Institute of Information Science, Academia Sinica From mobile phones to wearable health monitors, improved energy efficiency is the enabling technology of everything we take for granted today. Computer vision is at the center of artificial intelligence and machine learning. Today, artificial intelligence and low power are often at different ends of the spectrum. Low-power computer vision will enable greater adoption of the technologies in battery-powered IoT (Internet of Things) systems. This book collects the winners' solutions of the Low-Power Computer Vision Challenge and provides insight on how to improve efficiency of artificial intelligence. -- Edwin Park, Principal Engineer at Qualcomm
Illustrated
Yes
Dewey Decimal
006.37
Table Of Content
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
Synopsis
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., 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.
LC Classification Number
TA1634.L69 2022
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AlibrisBooks
99% positive Bewertungen•2,0 Mio. Artikel verkauft
Angemeldet als gewerblicher Verkäufer
Verkäuferbewertungen (543.435)
- e***u (283)- Bewertung vom Käufer.Letzter MonatBestätigter KaufThe listing was for a hardcover version of this book; however, I received a paperback. The Seller replied quickly to my question about this issue and issued a full refund - and let me keep the book. So, a diligent Seller for sure - and well packaged and reasonable timing on shipping. Thank you for the refund, and as you suggested, I'll likely donate this volume and seek the hardcover.
- e***n (392)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufGreat transaction, exactly as described, packed well, and promptly shipped on August 6th. Unfortunately the U.S. Postal Service took 23 calendar days to deliver the book. It was shipped from Pennsylvania, to Atlanta, past Alabama to Texas, enjoyed several days in Texas, then to Minneapolis, Jacksonville, Florida, back to Atlanta, finally to Birmingham, and Huntsville. The seller was very responsive and I decided it was interesting to see if/how the book would arrive. Thanks, Joe
- 0***g (380)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufExcellent purchase. Was able to get all three items from the one seller. Seller was able to bundle all three items together into one package. Items as described and arrived in perfect condition. Good communication around shipping and tracking as items delayed and not able to be delivered by original estimate. Thanks to shipping updates I was able to track the items arriving before the extended delivery time. Thank you for making these items available on EBay.