Multivariate Legendre-SNN on Loihi-2 for Time Series Classification and 5G Jamming Detection

5G-&-Beyond technologies offer the promise of improved speed and bandwidth, ultra low latency, high network reliability, and have the potential to enable new applications and services. It only seems fitting to complement the transformative future of 5G-&-Beyond with thelow energyoffering of Spiking Neural Networks (SNNs) on neuromorphic chips. In this work, we develop Loihi-2 (Intel’s neuromorphic chip) -compatible versions of our previously proposed Legendre-SNN model for univariate and multivariate Time-Series Classification (TSC), as well as for 5G wireless applications. The Legendre-SNN is a reservoir-based SNN, where, thenon-spikingLegendre Delay Network (LDN) is used as a static reservoir, followed by a trainablespikingnetwork. Deploying such an SNN model (mix ofnon-spikingandspikingcomponents)entirelyon Loihi-2 is nontrivial - this is due to the scarcity of related approaches and technical documentations. In this work, we present our approach and the technicalities of implementing thenon-spikingLDN on the rarely used “Lakemont core” (embedded on Loihi-2); thereby, adding to the scarce technical documentation to program on-chip Lakemont cores. Thus, our presented approach can be leveraged by other researchers as well - to implement theirnon-spikingcomponents right on-chip. Our proposedhardware-friendly versionsof Legendre-SNN when evaluated on Loihi-2, outperform LSTM-based models (- executed onGPU) on 7 of 24 TSC datasets. Here, we also emphasize on the applications of our Legendre-SNN versions for 5G Jamming Detection on Loihi-2, and complement it with a real-time video demonstration of Jamming Detection (with simulated signals) on our physical Kapoho-Point Single Chip Loihi-2 board, followed by detailed energy-analysis. Overall, this work is directed towards the (comparatively) understudied technical side of neuromorphic computing to enable researchers leverage the Lakemont cores and deploy their SNNsentirelyon Loihi-2, with a push towards the cause for neuromorphics in Wireless Communications.