Science China Technological Sciences

, Volume 56, Issue 2, pp 509–517 | Cite as

Parallel architecture and optimization for discrete-event simulation of spike neural networks

  • YuHua Tang
  • BaiDa Zhang
  • JunJie Wu
  • TianJiang Hu
  • Jing Zhou
  • FuDong Liu


Spike neural networks are inspired by animal brains, and outperform traditional neural networks on complicated tasks. However, spike neural networks are usually used on a large scale, and they cannot be computed on commercial, off-the-shelf computers. A parallel architecture is proposed and developed for discrete-event simulations of spike neural networks. Furthermore, mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational analysis. Simulation results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.


spike neural network discrete event simulation intelligent parallelization framework 


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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • YuHua Tang
    • 1
    • 2
  • BaiDa Zhang
    • 1
    • 2
  • JunJie Wu
    • 1
    • 2
  • TianJiang Hu
    • 3
  • Jing Zhou
    • 1
    • 2
  • FuDong Liu
    • 1
    • 2
  1. 1.Department of Computer Science and Technology, School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.College of Mechatronic Engineering and AutomationNational University of Defense TechnologyChangshaChina

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