Neuromorphic Computing With Address-Event-Representation Using Time-to-Event Margin Propagation

Abstract

Address-Event-Representation (AER) is a spike-routing protocol that allows the scaling of neuromorphic and spiking neural network (SNN) architectures. However, in conventional neuromorphic architectures, the AER protocol and in general, any virtual interconnect plays only a passive role in computation, i.e., only for routing spikes and events. In this paper, we show how causal temporal primitives like delay, triggering, and sorting inherent in the AER protocol itself can be exploited for scalable neuromorphic computing using our proposed technique called Time-to-Event Margin Propagation (TEMP). The proposed TEMP-based AER architecture is fully asynchronous and relies on interconnect delays for memory and computing as opposed to conventional and local multiply-and-accumulate (MAC) operations. We show that the time-based encoding in the TEMP neural network produces a spatio-temporal representation that can encode a large number of discriminatory patterns. As a proof-of-concept, we show that a trained TEMP-based convolutional neural network (CNN) can demonstrate an accuracy greater than 99% on the MNIST dataset and 91.2% on the Fashion MNIST Dataset. Overall, our work is a biologically inspired computing paradigm that brings forth a new dimension of research to the field of neuromorphic computing.