Neuromorphic Wireless Semantic Communication with Multi-Level Spikes

Neuromorphic computing, inspired by biological processes, leverages spiking neural networks (SNNs) for efficient inference with sequential data. Recent advances demonstrate that embedding bits within each spike exchanged between neurons can boost accuracy. In a split computing architecture with neuromorphic semantic communication, in which the SNN spans two devices connected wirelessly, the first device must transmit spike information from its output neurons to the second device. This setup requires balancing the benefits of multilevel spikes with the challenges of wirelessly transmitting additional bits between devices. This paper explores a neuromorphic wireless semantic communication architecture with multi-level SNNs, introducing a digital modulation scheme optimized for an orthogonal frequency-division multiplexing (OFDM) radio interface. Simulations reveal performance gains from multi-level SNN models and identify the optimal payload size based on transmitter-receiver connection quality.