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Classification of Database by Using Parallelization of Algorithms Third Generation in a GPU

  • Israel Tabarez Paz
  • Neil Hernández Gress
  • Miguel González Mendoza
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

This manuscript is focused on the efficiency analysis of Artificial Neural Networks (ANN) that belongs to the third generation, which are Spiking Neural Networks (SNN) and Support Vector Machine (SVM). The main issue of scientific community have been to improve the efficiency of ANN. So, we applied architecture GPU (Graphical Processing Unit) from NVIDIA model GeForce 9400M. On the other hand, the results of QP method for SVM depends on computational complexity of the algorithm, which is proportional to the volume and attributes of the data. Moreover, SNN was selected because it is a method that has not been explored fully. Despite the economic cost is very high in parallel programming, this is compensated with the large number of real applications such as clustering and pattern recognition. In the state of the art, nobody of authors has coded Quadratic Programming (QP) of SVM in a GPU. In case of SNN, it has been developed by using a specific software as MATLAB, FPGA or sequential circuits but it have never been coded in a GPU. Finally, it is necessary to reduce the grade of parallelization caused by limitations of hardware.

Keywords

GPU CPU Artificial Neural Networks Spiking Neural Networks Support Vector Machine Classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Israel Tabarez Paz
    • 1
  • Neil Hernández Gress
    • 2
  • Miguel González Mendoza
    • 2
  1. 1.Universidad Autónoma del Estado de MéxicoAtizapán de ZaragozaMéxico
  2. 2.Tecnológico de Monterrey, Campus Estado de MéxicoAtizapán de ZaragoraMéxico

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