Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yu:2021:10.1109/ICFPT52863.2021.9609813,
author = {Yu, Z and Bouganis, C-S},
doi = {10.1109/ICFPT52863.2021.9609813},
pages = {69--77},
publisher = {IEEE},
title = {StreamSVD: Low-rank approximation and streaming accelerator co-design},
url = {http://dx.doi.org/10.1109/ICFPT52863.2021.9609813},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The post-training compression of a Convolutional Neural Network (CNN) aims to produce Pareto-optimal designs on the accuracy-performance frontier when the access to training data is not possible. Low-rank approximation is one of the methods that is often utilised in such cases. However, existing work considers the low-rank approximation of the network and the optimisation of the hardware accelerator separately, leading to systems with sub-optimal performance. This work focuses on the efficient mapping of a CNN into an FPGA device, and presents StreamSVD, a model-accelerator co-design framework 1 . The framework considers simultaneously the compression of a CNN model through a hardware-aware low-rank approximation scheme, and the optimisation of the hardware accelerator's architecture by taking into account the approximation scheme's compute structure. Our results show that the co-designed StreamSVD outperforms existing work that utilises similar low-rank approximation schemes by providing better accuracy-throughput trade-off. The proposed framework also achieves competitive performance compared with other post-training compression methods, even outperforming them under certain cases.
AU  - Yu,Z
AU  - Bouganis,C-S
DO  - 10.1109/ICFPT52863.2021.9609813
EP  - 77
PB  - IEEE
PY  - 2021///
SP  - 69
TI  - StreamSVD: Low-rank approximation and streaming accelerator co-design
UR  - http://dx.doi.org/10.1109/ICFPT52863.2021.9609813
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000792703100010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://ieeexplore.ieee.org/document/9609813
UR  - http://hdl.handle.net/10044/1/104864
ER  -
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