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

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.