A hybrid GPU-FPGA-based computing platform for machine learning

Liu, Xu, Ounifi, Hibat Allah, Gherbi, Abdelouahe, Lemieux, Yves et Li, Wubin. 2018. « A hybrid GPU-FPGA-based computing platform for machine learning ». In The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2018) / The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2018) (Leuven, Belgium, Nov. 5-8, 2018) Coll. « Procedia Computer Science », vol. 141. pp. 104-111. Elsevier B.V..
Compte des citations dans Scopus : 17.

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Abstract

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.

Item Type:	Conference proceeding
ISBN:	18770509
Professor:	Professor Gherbi, Abdelouahed
Affiliation:	Génie logiciel et des technologies de l'information
Date Deposited:	22 Jan 2019 15:25
Last Modified:	12 Jul 2019 20:14
URI:	https://espace2.etsmtl.ca/id/eprint/17955

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