SNEAP: A fast and efficient toolchain for mapping large-scale spiking neural network onto noc-based neuromorphic platform

Abstract

Spiking neural network (SNN), as the third generation of artificial neural networks, has been widely adopted in vision and audio tasks. Nowadays, many neuromorphic platforms support SNN simulation and adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, a large volume and run-time communication on the interconnection has a significant effect on performance of the platform. In this paper, we propose a toolchain called SNEAP (Spiking NEural network mAPping toolchain) for mapping SNNs to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection. SNEAP includes two key steps: partitioning the SNN to reduce the spikes communicated between partitions, and mapping the partitions of SNN to the NoC to reduce average hop of spikes under the constraint of hardware resources. SNEAP effectively reduces the energy and latency on the NoC-based neuromorphic platform and spend less time than other toolchains. The experimental results show that SNEAP can achieve average 418× reduction in end-to-end execution time, and reduce energy consumption and spike latency, on average, by 23% and 51% respectively, compared with SpiNeMap.