Imperial College London

Professor Peter Y. K. Cheung

Faculty of EngineeringDyson School of Design Engineering

Head of the Dyson School of Design Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6200p.cheung Website

 
 
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Assistant

 

Mrs Wiesia Hsissen +44 (0)20 7594 6261

 
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Location

 

910BElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wang:2019:10.1145/3309551,
author = {Wang, E and Davis, J and Zhao, R and Ng, H and Niu, X and Luk, W and Cheung, P and Constantinides, G},
doi = {10.1145/3309551},
journal = {ACM Computing Surveys},
pages = {40:1--40:39},
title = {Deep neural network approximation for custom hardware: where we've been, where we're going},
url = {http://dx.doi.org/10.1145/3309551},
volume = {52},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have become a hot topic. Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency. Application-tailored accelerators, when co-designed with approximation-based network training methods, transform large, dense and computationally expensive networks into small, sparse and hardware-efficient alternatives, increasing the feasibility of network deployment. In this article, we provide a comprehensive evaluation of approximation methods for high-performance network inference along with in-depth discussion of their effectiveness for custom hardware implementation. We also include proposals for future research based on a thorough analysis of current trends. This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope to inspire exciting new developments in the field.
AU - Wang,E
AU - Davis,J
AU - Zhao,R
AU - Ng,H
AU - Niu,X
AU - Luk,W
AU - Cheung,P
AU - Constantinides,G
DO - 10.1145/3309551
EP - 1
PY - 2019///
SN - 0360-0300
SP - 40
TI - Deep neural network approximation for custom hardware: where we've been, where we're going
T2 - ACM Computing Surveys
UR - http://dx.doi.org/10.1145/3309551
UR - https://arxiv.org/abs/1901.06955
UR - https://dl.acm.org/citation.cfm?id=3309551
UR - http://hdl.handle.net/10044/1/67297
VL - 52
ER -