A GPU based simulation of multilayer spiking neural networks

Nowadays, despite significant advances in VLSI technology, in the case of massively parallel systems still new computational architectures are required. Using graphic processing units (GPU) as a low-cost and high performance computing platform is an efficient preferred approach to such problems. Simulation of spiking neural networks (SNN) is a well-known challenge encountering these barriers. In this paper we demonstrate an Izhikevich neuron simulator that runs on a single GPU. The GPU-SNN model (running on an NVIDIA GT325M with 1GB of memory) is up to 11 times faster than a CPU version when more than one million neurons with 75 billion synaptic connections. Simulation results are compared for different single GPU with the CPU based simulation different single GPU. Simulation method is based on a new method of virtual synaptic computation, which performs the calculation with low memory usage.