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@inproceedings{Zhao:2018:10.1109/FPL.2018.00033,
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 = {http://dx.doi.org/10.1109/FPL.2018.00033},
year = {2018}
}

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TY  - CPAPER
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
PB  - IEEE
PY  - 2018///
TI  - Towards efficient convolutional neural network for domain-specific applications on FPGA
UR  - http://dx.doi.org/10.1109/FPL.2018.00033
UR  - https://ieeexplore.ieee.org/document/8533484
UR  - http://hdl.handle.net/10044/1/62190
ER  -

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