深度卷积神经网络高效计算研究进展

程健; 王培松; 李钢; 胡庆浩; 卢汉清

doi:10.1631/FITEE.1700789

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深度卷积神经网络高效计算研究进展

2018 Special Issue on Artificial Intelligence 2.0: Theories and Applications | Updated：2022-05-19

- 深度卷积神经网络高效计算研究进展
- Recent advances in efficient computation of deep convolutional neural networks
- 信息与电子工程前沿（英文） 2018年19卷第1期页码：64-77
- Affiliations：
  
  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  CHENG Jian, E-mail: jcheng@nlpr.ia.ac.cn
  [ "WANG Pei-song, E-mail:peisong.wang@nlpr.ia.ac.cn" ]
  [ "LI Gang, E-mail: gang.li@nlpr.ia.ac.cn" ]
  [ "HU Qing-hao, E-mail: qinghao.hu@nlpr.ia.ac.cn" ]
- Funds：
- DOI：10.1631/FITEE.1700789
  中图分类号： TP3
- 收稿：2017-11-25，
  
  修回：2018-;1-26，
  
  纸质出版：2018-01
- Accepted：
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程健, 王培松, 李钢, 等. 深度卷积神经网络高效计算研究进展[J]. 信息与电子工程前沿（英文）, 2018,19(1):64-77.

Jian CHENG, Pei-song WANG, Gang LI, et al. Recent advances in efficient computation of deep convolutional neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 64-77.
程健, 王培松, 李钢, 等. 深度卷积神经网络高效计算研究进展[J]. 信息与电子工程前沿（英文）, 2018,19(1):64-77. DOI： 10.1631/FITEE.1700789.

Jian CHENG, Pei-song WANG, Gang LI, et al. Recent advances in efficient computation of deep convolutional neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 64-77. DOI： 10.1631/FITEE.1700789.

摘要

近年来迅速发展的深度神经网络已成为许多智能系统的基础工具。同时，深度网络的计算复杂度和资源消耗也在持续增加，这给深度网络的部署带来了严峻挑战，尤其在实时应用中或应用设备资源有限时。因此，网络加速是深度学习领域的热门话题。为提升深度神经网络的硬件性能，最近几年涌现出一大批基于现场可编程门阵列（field-programmable gate array

FPGA）或专用集成电路（application-specific integrated circuit

ASIC）的加速器。本文针对网络加速、压缩、软硬件结合的加速器设计等方面的进展进行了详细而全面的总结。特别地，本文对网络剪枝、低秩估计、网络量化、拟合网络、紧凑网络设计以及硬件加速器进行了深入分析。最后，展望了该领域未来一些研究方向。

Abstract

Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems. At the same time

the computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks

especially in real-time applications or on resource-limited devices. Thus

network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks

a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper

we provide a comprehensive survey of recent advances in network acceleration

compression

and accelerator design from both algorithm and hardware points of view. Specifically

we provide a thorough analysis of each of the following topics: network pruning

low-rank approximation

network quantization

teacherɃstudent networks

compact network design

and hardware accelerators. Finally

we introduce and discuss a few possible future directions.

关键词

Keywords

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