Efficient Processing of Deep Neural Networks / by Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer.

Format:

Book

Author/Creator:

Sze, Vivienne, Author.

Chen, Yu-Hsin., Author.

Yang, Tien-Ju, Author.

Emer, Joel S., Author.

Series:

Synthesis Lectures on Computer Architecture, 1935-3243

Language:

English

Subjects (All):

Electronic circuits.

Microprocessors.

Computer architecture.

Electronic Circuits and Systems.

Processor Architectures.

Local Subjects:

Electronic Circuits and Systems.

Processor Architectures.

Physical Description:

1 online resource (XXI, 254 p.)

Edition:

1st ed. 2020.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

Summary:

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.

Contents:

Preface

Acknowledgments

Introduction

Overview of Deep Neural Networks

Key Metrics and Design Objectives

Kernel Computation

Designing DNN Accelerators

Operation Mapping on Specialized Hardware

Reducing Precision

Exploiting Sparsity

Designing Efficient DNN Models

Advanced Technologies

Conclusion

Bibliography

Authors' Biographies.

ISBN:

9783031017667

3031017668