Advertisement

GPU Implementation of Spiking Neural Networks for Edge Detection

  • Zhiqiang Zhuo
  • Qingxiang Wu
  • Zhenmin Zhang
  • Gongrong Zhang
  • Liuping Huang
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

Spiking neural networks (SNN) are effective model inspired by neural networks in the brain. However, when networks increase in size towards the biological scale, it is time-consuming to simulate the networks using CPU programming. To solve this problem, Graphic Processing Units (GPU) provide a method to speed up the simulation. It is proposed and proved as a pertinent solution for implementation of large scale of neural networks. This paper presents a GPU implementation of SNN for edge detection. The approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach provide about 37 times faster than the CPU implementation.

Keywords

Graphic processing units spiking neural network edge detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Xie, E.M., McGinnity, T.M., Wu, Q.X.: GPU implementation of spiking neural networks for color image segmentation. Image and Signal Processing 2011 3, 1246–1250 (2011)CrossRefGoogle Scholar
  2. 2.
    Maguire, L.P., McGinnity, T.M., Glackin, B., Ghani, A., Belatreche, A., Harkin, J.: Challenges for large-scale implementations of spiking neural networks on FPGAs. Neurocomputing 71, 13–29 (2007)CrossRefGoogle Scholar
  3. 3.
    Lzhikevich, E.M.: Simple Model of Spiking Neurons. IEEE Trans on Neural Networks 14, 1569–1572 (2003)CrossRefGoogle Scholar
  4. 4.
    Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, populations, Plasticity. Cambridge University Press (2002)Google Scholar
  5. 5.
    Nageswaran, J.M., Dutt, N., Krichmar, J.L.: A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphicsprocessors. Neural Networks 22, 5–6 (2009)CrossRefGoogle Scholar
  6. 6.
    Bernhard, F., Keriven, R.: Spiking Neurons on GPUs. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 236–243. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    NVIDIA Tesla C2075 companion processor calculate results exponentially faster, http://www.nvidia.co.uk/content/PDF/datasheet/NV_D_Tesla_C2075_Sept11_US_HR.pdf
  9. 9.
    Wu, Q.X., McGinnity, T.M., Maguire, L., Belatreche, A., Glackin, B.: Edge Detection Based on Spiking Neural Network Model. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS (LNAI), vol. 4682, pp. 26–34. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Dempster, P., McGinnity, T.M., Glackin, B., Wu, Q.X.: Performance Comparison Of a Biologically Inspired Edge Detection Algorithm On CPU, GPU And FPGA. In: International Conference on Fuzzy Computation, pp. 420–424. SCITePress, Valencia (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhiqiang Zhuo
    • 1
  • Qingxiang Wu
    • 1
  • Zhenmin Zhang
    • 1
  • Gongrong Zhang
    • 1
  • Liuping Huang
    • 1
  1. 1.College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhouChina

Personalised recommendations