Non-Traditional Input Encoding Schemes for Spiking Neuromorphic Systems

Abstract

A key challenge for utilizing spiking neural networks or spiking neuromorphic systems for most applications is translating numerical data into spikes that are appropriate to apply as input to a spiking neural network. In this work, we present several approaches for encoding numerical values as spikes, including binning, spike-count encoding, and charge-injection encoding, and we show how these approaches can be combined hierarchically to form more complex encoding schemes. We demonstrate how these different encoding approaches perform on four different applications, running on four different neuromorphic systems that are based on spiking neural networks. We show that the input encoding method can have a significant effect on application performance and that the best input encoding method is application-specific.