Parallel Neural Network Training with OpenCL
Faculty of Electrical Engineering and Computing, Unska 3, Zagreb, Croatia
International Convention MIPRO, 2012
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.
June 23, 2012 by hgpu
