Fuzzy ART Neural Network Parallel Computing on the GPU
Higher School of Telecommunications Engineering, University of Valladolid, Spain
Computational and Ambient Intelligence (2007), pp. 463-470.
@conference{martinez2007fuzzy,
title={Fuzzy ART neural network parallel computing on the GPU},
author={Mart{‘i}nez-Zarzuela, M. and Pernas, F.J.D. and Higuera, J.F.D. and Rodr{‘i}guez, M.A.},
booktitle={Proceedings of the 9th international work conference on Artificial neural networks},
pages={463–470},
year={2007},
organization={Springer-Verlag}
}
Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU is presented. Experimental results show training process is slower on the GPU than on a dual-core Pentium 4 at 3.2 GHz. Once the Neural Network has been trained, the proposed design manages to accelerate Fuzzy ART testing process up to 33 times on a GeForce 7800GT graphics card.
October 28, 2010 by hgpu
