Imperial College London

ProfessorWayneLuk

Faculty of EngineeringDepartment 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{Zhao:2017:10.1109/ISVLSI.2017.127,
author = {Zhao, R and Luk, W and Niu, X and Shi, H and Wang, H},
doi = {10.1109/ISVLSI.2017.127},
pages = {645--650},
title = {Hardware Acceleration for Machine Learning},
url = {http://dx.doi.org/10.1109/ISVLSI.2017.127},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © 2017 IEEE. This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data analysis. The potential of the proposed approach is presented.
AU - Zhao,R
AU - Luk,W
AU - Niu,X
AU - Shi,H
AU - Wang,H
DO - 10.1109/ISVLSI.2017.127
EP - 650
PY - 2017///
SN - 2159-3469
SP - 645
TI - Hardware Acceleration for Machine Learning
UR - http://dx.doi.org/10.1109/ISVLSI.2017.127
ER -