Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence (Chapman & Hall/CRC Computer Vision)

ISBN 10: 0367744708 / ISBN 13: 9780367744700

Verlag: Chapman and Hall/CRC, 2022

Beschreibung

Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.

Bestandsnummer des Verkäufers GWSVV.0367744708.G

Bibliografische Details

• Titel: Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence (Chapman & Hall/CRC Computer Vision)
• Verlag: Chapman and Hall/CRC
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 0367744708
• ISBN-13: 9780367744700
• Einband: Hardcover
• Zustand: good

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.