Generative Data for Neuromorphic Computing

Abstract

Neuromorphic computing is a next-generation model of computation that leverages biologically-inspired artificial neurons to perform complex tasks. Unlike neurons found within traditional Artificial Neural Networks (ANNs), which output on a continuous range, neuromorphic neurons found within Spiking Neural Networks (SNNs) output discrete spikes over time, similar to the individual firing of biological neurons. This low Size Weight and Power (SWaP) model of computation, when realized on specialized neuromorphic hardware and coupled with low-power neuromorphic sensors, makes neuromorphic computing an ideal candidate for edge computing applications. However, training neuromorphic models is challenging because of the scarcity of quality neuromorphic datasets. In this paper, we present a platform-agnostic approach for creating synthetic neuromorphic datasets with a Conditional Generative Adversarial Network (CGAN). Neuromorphic models trained on the generated datasets perform comparably to those trained on the original IBM DVSGesture dataset. We show that neuromorphic dataset generation produces quality samples which can further aid the development and deployment of neuromorphic computing models.