SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform
Shiming Li, Shasha Guo, Limeng Zhang, Ziyang Kang, Shiying Wang, Wei Shi, Lei Wang, Weixia Xu
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.