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Speed-Up Method for Neural Network Learning Using GPGPU

  • Yuta Tsuchida
  • Michifumi Yoshioka
  • Sigeru Omatu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

GPU is the dedicated circuit to draw the graphics, so it has a characteristic that the many simple arithmetic circuits are implemented. This characteristic is hoped to apply the massive parallelism not only graphic processing. In this paper, the neural network, one of the pattern recognition algorithms is applied to be faster by using GPU. In the learning of the neural network, there are many points to be processed at the same time. We propose a method which makes the neural network be parallelized in three points. The parallelizations are implemented in neural networks which have different initial weight coefficients, the learning patterns or neurons in a layer of neural network. These methods are used in combination, but the first method can be processed independently. Therefore one of the three methods, the first method, is employed as comparison to compare with the proposed methods. As the result, the proposed method is 6 times faster than comparison method.

Keywords

Neural Network Hide Layer Graphic Processing Unit Global Memory Neural Network Training 
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 2012

Authors and Affiliations

  • Yuta Tsuchida
    • 1
  • Michifumi Yoshioka
    • 1
  • Sigeru Omatu
    • 2
  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan
  2. 2.Department of Electronics, Information and Communication Engineering Faculty of EngineeringOska Institute of TechnologyOsakaJapan

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