Imperial College London
Professor Wayne Luk

ProfessorWayneLuk

Faculty of Engineering, Department of Computing

Professor of Computer Engineering
 
 
 
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Contact

 

+44 (0)20 7594 8313w.luk Website

 
 
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Location

 

434Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Voss:2017:10.1109/ICCD.2017.77,
author = {Voss, N and Bacis, M and Mencer, O and Gaydadjiev, G and Luk, W},
doi = {10.1109/ICCD.2017.77},
pages = {435--438},
publisher = {IEEE},
title = {Convolutional Neural Networks on Dataflow Engines},
url = {http://dx.doi.org/10.1109/ICCD.2017.77},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In this paper we discuss a high performance implementation for Convolutional Neural Networks (CNNs) inference on the latest generation of Dataflow Engines (DFEs). We discuss the architectural choices made during the design phase taking into account the DFE chip properties. We then perform design space exploration, considering the memory bandwidth and resources utilisation constraints derived from the used DFE and the chosen architecture. Finally, we discuss the high performance implementation and compare the obtained performance against other implementations, showing that our proposed design reaches 2,450 GOPS when running VGG16 as a test case.
AU  - Voss,N
AU  - Bacis,M
AU  - Mencer,O
AU  - Gaydadjiev,G
AU  - Luk,W
DO  - 10.1109/ICCD.2017.77
EP  - 438
PB  - IEEE
PY  - 2017///
SN  - 1063-6404
SP  - 435
TI  - Convolutional Neural Networks on Dataflow Engines
UR  - http://dx.doi.org/10.1109/ICCD.2017.77
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000424789300067&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/61591
ER  -
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