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[ DevCourseWeb.com ] Efficient Processing of Deep Neural Networks
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Morgan & Claypool | English | 2020 | ISBN-10: 1681738317 | 341 pages | PDF | 20.36 MB
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.
The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
About the Author
Vivienne Sze received a B.A.Sc. (Hons) degree in Electrical Engineering from the University of Toronto, Toronto, ON, Canada, in 2004, and S.M. and Ph.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT.
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