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Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA

  • M. Martínez-Zarzuela
  • F. J. Díaz Pernas
  • A. Tejero de Pablos
  • M. Antón Rodríguez
  • J. F. Díez Higuera
  • D. Boto Giralda
  • D. González Ortega
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)

Abstract

Programming of Graphics Processing Units (GPUs) has evolved in a way they can be used to address and speed-up computation of algorithms exemplified by data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.

Keywords

Graphic Processing Unit Shared Memory Input Pattern Global Memory Cellular Neural Network 
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 2009

Authors and Affiliations

  • M. Martínez-Zarzuela
    • 1
  • F. J. Díaz Pernas
    • 1
  • A. Tejero de Pablos
    • 1
  • M. Antón Rodríguez
    • 1
  • J. F. Díez Higuera
    • 1
  • D. Boto Giralda
    • 1
  • D. González Ortega
    • 1
  1. 1.Higher School of Telecommunications EngineeringUniversity of ValladolidSpain

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