Parallel neural network training with OpenCL

Author

Krpan, N. ; Jakobovic, Domagoj

fYear

2012

fDate

21-25 May 2012

Firstpage

1053

Lastpage

1057

Abstract

This paper describes the parallelization of neural network training algorithms on heterogeneous architectures with graphical processing units (GPU). The algorithms used for training are particle swarm optimization and backpropagation. Parallel versions of both methods are presented and speedup results are given as compared to the sequential version. The efficiency of parallel training is investigated in regards to various neural network and training parameters.

Keywords

backpropagation; graphics processing units; neural nets; parallel processing; particle swarm optimisation; OpenCL; backpropagation; heterogeneous architectures; parallel neural network training algorithm; parallel training; parallelization; particle swarm optimization; Backpropagation; Biological neural networks; Graphics processing unit; Memory management; Neurons; Random access memory; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

MIPRO, 2012 Proceedings of the 35th International Convention

Conference_Location

Opatija

Print_ISBN

978-1-4673-2577-6

Type

conf

Filename

6240799

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=565040