Deep Spiking Binary Neural Network for Digital Neuromorphic Hardware

Abstract

The spiking neural network (SNN) converted from artificial neural network (ANN) usually contains many high-precision parameters. This will cause a lot of hardware resources to be consumed. A spiking binary neural network is proposed in this paper, whose weights are binary and it does not contain high-precision parameters. Experimental results show that the proposed network can be adapted to the neuromorphic chip and reduce memory consumption by 16 times without much loss of accuracy.