Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Engineering



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




434Huxley BuildingSouth Kensington Campus






BibTex format

author = {Zhao, R and Ng, H-C and Luk, W and Niu, X},
doi = {10.1109/FPL.2018.00033},
publisher = {IEEE},
title = {Towards efficient convolutional neural network for domain-specific applications on FPGA},
url = {},
year = {2018}

RIS format (EndNote, RefMan)

AB - FPGA becomes a popular technology for imple-menting Convolutional Neural Network (CNN) in recent years.Most CNN applications on FPGA are domain-specific, e.g.,detecting objects from specific categories, in which commonly-used CNN models pre-trained on general datasets may not beefficient enough. This paper presents TuRF, an end-to-end CNNacceleration framework to efficiently deploy domain-specific ap-plications on FPGA by transfer learning that adapts pre-trainedmodels to specific domains, replacing standard convolution layerswith efficient convolution blocks, and applying layer fusion toenhance hardware design performance. We evaluate TuRF bydeploying a pre-trained VGG-16 model for a domain-specificimage recognition task onto a Stratix V FPGA. Results showthat designs generated by TuRF achieve better performance thanprior methods for the original VGG-16 and ResNet-50 models,while for the optimised VGG-16 model TuRF designs are moreaccurate and easier to process.
AU - Zhao,R
AU - Ng,H-C
AU - Luk,W
AU - Niu,X
DO - 10.1109/FPL.2018.00033
PY - 2018///
TI - Towards efficient convolutional neural network for domain-specific applications on FPGA
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ER -