Efficient Processing of Deep Neural Networks
Vivienne Sze, Massachusetts Institute of Technology
Yu-Hsin Chen, Massachusetts Institute of Technology
Tien-Ju Yang, Massachusetts Institute of Technology
Joel S. Emer, Massachusetts Institute of Technology and Nvidia Research
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 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 the DNN processing that are amenable to hardware/algorithmco-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 a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
Morgan & Claypool Publishers
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