A Large Scale Digital Simulation of Spiking Neural Networks (SNN) on Fast SystemC Simulator

Since biological neural systems contain big number of neurons working in parallel, simulation of such dynamic system is a real challenge. The main objective of this paper is to speed up the simulation performance of SystemC designs at the RTL abstraction level using the high degree of parallelism afforded by graphics processors (GPUs) for large scale SNN with proposed structure in pattern classification field. Simulation results show 100 times speedup for the proposed SNN structure on the GPU compared with the CPU version. In addition, CPU memory has problems when trained for more than 120K cells but GPU can simulate up to 40 million neurons.