Fuzzy ART neural network parallel computing on the GPU

Authors:
Mario Martínez-Zarzuela;Francisco Javier Díaz Pernas;Josél Fernando Dííez Higuera;Míriam Antón Rodríguez
Affiliations:
Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain;Higher School of Telecommunications Engineering, University of Valladolid, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

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.