  Address-Event-Representation (AER) is a spike-routing protocol that allows
the scaling of neuromorphic and spiking neural network (SNN) architectures to a
size that is comparable to that of digital neural network 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. Overall, our
work is a biologically inspired computing paradigm that brings forth a new
dimension of research to the field of neuromorphic computing.
