18600

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Xu Liu, Hibat Allah Ounifi, Abdelouahed Gherbi, Yves Lemieux, Wubin Li
Department of Software and IT Engineering, Ecole de Technologie Superieure (ETS), Montreal, Canada
Procedia Computer Science, Volume 141, Pages 104-111, 2018

@article{liu2018hybrid,

   title={A Hybrid GPU-FPGA-based Computing Platform for Machine Learning},

   author={Liu, Xu and Ounifi, Hibat Allah and Gherbi, Abdelouahed and Lemieux, Yves and Li, Wubin},

   journal={Procedia Computer Science},

   volume={141},

   pages={104–111},

   year={2018},

   publisher={Elsevier}

}

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We present a hybrid GPU-FPGA based computing platform to tackle the high-density computing problem of machine learning. In our platform, the training part of a machine learning application is implemented on GPU and the inferencing part is implemented on FPGA. It should also include a model transplantation part which can transplant the model from the training part to the inferencing part. For evaluating this design methodology, we selected the LeNet-5 as our benchmark algorithm. During the training phase, GPU TitanXp’s speed was about 8.8x faster than CPU E-1620 and in the inferencing phase, FPGA Arria-10’s inferencing speed was fastest, 44.4x faster than CPU E-1620 and 6341x faster than GPU TitanXp. Moreover, by adopting our design methodology, we improved our LeNet-5 machine learning model’s accuracy from 99.05% to 99.13%, and successfully preserved the accuracy (99.13%) when transplanting the model from the GPU platform to the FPGA platform.
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