SWsnn: A Novel Simulator for Spiking Neural Networks.

Abstract

Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed. However, due to the randomness of connections between neurons and spiking events in SNN simulation, this causes a lot of memory access time. To alleviate this problem, we developed an SNN simulator SWsnn based on the new Sunway SW26010pro processor. The SW26010pro processor consists of six core groups, each with 16 MB of local data memory (LDM). LDM has the characteristics of high-speed read and write, which is suitable for performing simulation tasks similar to SNNs. Experimental results show that SWsnn runs faster than other mainstream GPU-based simulators when simulating a certain scale of neural network, showing a strong performance advantage. To conduct larger scale simulations, SWsnn designed a simulation computation based on a large shared model of Sunway processor and developed a multiprocessor version of SWsnn based on this mode, achieving larger scale SNN simulations.