  This paper introduces a novel "all-spike" low-power solution for remote
wireless inference that is based on neuromorphic sensing, Impulse Radio (IR),
and Spiking Neural Networks (SNNs). In the proposed system, event-driven
neuromorphic sensors produce asynchronous time-encoded data streams that are
encoded by an SNN, whose output spiking signals are pulse modulated via IR and
transmitted over general frequence-selective channels; while the receiver's
inputs are obtained via hard detection of the received signals and fed to an
SNN for classification. We introduce an end-to-end training procedure that
treats the cascade of encoder, channel, and decoder as a probabilistic
SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The
proposed system, termed NeuroJSCC, is compared to conventional synchronous
frame-based and uncoded transmissions in terms of latency and accuracy. The
experiments confirm that the proposed end-to-end neuromorphic edge architecture
provides a promising framework for efficient and low-latency remote sensing,
communication, and inference.
