ENGLISH
La vitrine de diffusion des publications et contributions des chercheurs de l'ÉTS
RECHERCHER

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

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

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 : 18.

[thumbnail of Gherbi A 2018 17955.pdf]
Prévisualisation
PDF
Gherbi A 2018 17955.pdf - Version publiée
Licence d'utilisation : Creative Commons CC BY-NC-ND.

Télécharger (1MB) | Prévisualisation

Résumé

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.

Type de document: Compte rendu de conférence
ISBN: 18770509
Professeur:
Professeur
Gherbi, Abdelouahed
Affiliation: Génie logiciel et des technologies de l'information
Date de dépôt: 22 janv. 2019 15:25
Dernière modification: 12 juill. 2019 20:14
URI: https://espace2.etsmtl.ca/id/eprint/17955

Actions (Authentification requise)

Dernière vérification avant le dépôt Dernière vérification avant le dépôt