Spiking Neural Network Transformer for Deploying into a Deep Learning Framework

Abstract

Spiking neural network (SNNs) have been widely studied as an analysis model for human brain functioning. The energy-efficient nature of SNNs have attracted attentions of engineering researchers in deep neural networks. They sometimes need to have a tool that transforms SNNs to be executed in a deep learning framework. Due to inherent difference in their components for SNNs and deep neural networks, there are some inevitable restrictions in such transformations. This paper presents a new design and simulation environment for SNNs, which allows to build various architecture of SNNs and transforms them into computation graphs for execution. It supports several training algorithms for them. It exports their functionalities as APIs in Python with which the developers can build, train, and execute SNN models.