Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors

Abstract

Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, Izhikevich neuron based large-scale SNN simulator that runs on a single GPU. The GPU-SNN model (running on an NVIDIA GTX-280 with 1GB of memory), is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7Hz. For simulation of 100K neurons with 10 Million synaptic connections, the GPU-SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and compact network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results were validated against CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. We intend to make our simulator available to the modeling community so that researchers will have easy access to large-scale SNN simulations.