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Fuzzy ART Neural Network Parallel Computing on the GPU

  • Mario Martínez-Zarzuela
  • Francisco Javier Díaz Pernas
  • José Fernando Díez Higuera
  • Míriam Antón Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

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.

Keywords

Input Pattern Activity Vector Category Choice Output Texture Programmable Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mario Martínez-Zarzuela
    • 1
  • Francisco Javier Díaz Pernas
    • 1
  • José Fernando Díez Higuera
    • 1
  • Míriam Antón Rodríguez
    • 1
  1. 1.Higher School of Telecommunications Engineering, University of ValladolidSpain

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