Neuromorphic Non-Orthogonal Multiple Access for Parallel Remote Inference via Vector Symbolic Architecture

Jiechen Chen, Zihang Song, Dengyu Wu, Bipin Rajendran, Osvaldo Simeone

Emerging edge intelligence systems increasingly rely on dense deployments of always-on sensors that must convey task-relevant information to a remote model under tight energy and spectral budgets. The deployment of event-driven neuromorphic sensing paired with spiking neural networks (SNNs) is attractive in this regime because it produces dynamically sparse representations, so that energy is spent on communication and computation only when informative events occur. Prior multiple-access protocols for remote inference using neuromorphic sensing and computing targeted collaborative settings, in which the server fuses information from all devices into a single decision. This paper instead addresses parallel remote inference, in which each device observes a distinct input, and requires its own classification decision. We propose NOMA-NC, a non-orthogonal multiple-access (NOMA) neuromorphic communication (NC) protocol built on the vector symbolic architecture (VSA) framework. In NOMA-NC, each device binds its sparse spike feature map with a device-specific permutation key, and all devices in a group transmit concurrently so that the over-the-air superposition directly realizes the VSA bundling operation. A shared decoding SNN, together with lightweight per-device learned unbinding, recovers all decisions in a single inference pass. Experiments on the N-MNIST and DVS128 Gesture datasets show that NOMA-NC yields goodput gains and savings in terms of receiver computing energy that are sub-proportional to the number of simultaneously active devices, without increasing the per-device transmission energy.