Efficient Processing of Deep Neural Networks. Vivienne Sze
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Efficient Processing of Deep Neural Networks
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer
ISBN: 9781681738314 paperback
ISBN: 9781681738321 ebook
ISBN: 9781681738338 hardcover
DOI 10.2200/S01004ED1V01Y202004CAC050
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON COMPUTER ARCHITECTURE
Lecture #50
Series Editors: Natalie Enright Jerger, University of Toronto
Margaret Martonosi, Princeton University
Founding Editor Emeritus: Mark D. Hill, University of Wisconsin, Madison
Series ISSN
Print 1935-3235 Electronic 1935-3243
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Efficient Processing of Deep Neural Networks
Vivienne Sze, Yu-Hsin Chen, and Tien-Ju Yang
Massachusetts Institute of Technology
Joel S. Emer
Massachusetts Institute of Technology and Nvidia Research
SYNTHESIS LECTURES ON COMPUTER ARCHITECTURE #50
ABSTRACT
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.
KEYWORDS
deep learning, neural network, deep neural networks (DNN), convolutional neural networks (CNN), artificial intelligence (AI), efficient processing, accelerator architecture, hardware/software co-design, hardware/algorithm co-design, domain-specific accelerators
Contents
PART I Understanding Deep Neural Networks
1.1 Background on Deep Neural Networks
1.1.1 Artificial Intelligence and Deep Neural Networks