SNN-IDS: Deploying SNN-Equivalent Intrusion Detection on a Commodity MCU NPU

We deploy a spiking neural network (SNN)-equivalent intrusion detection system (IDS) on the STM32N6570-DK, a commodity ARM Cortex-M55 MCU with the Neural-ART NPU. Exploiting the approximate equivalence between single-timestep (T=1) SNN inference and INT8 quantized ANN inference, we compile a lightweight MLP classifier to the NPU without neuromorphic hardware. Evaluated on NSL-KDD (5-class) and UNSW-NB15 (10-class) with 10 random seeds, the ReLU model achieves 78.86±1.32% and 64.75±0.61% overall accuracy, respectively. INT8 accuracy stays within 1 percentage point of FP32 across all 24 tested calibration configurations, and layer-wise analysis shows 99.0% final prediction agreement between FP32 and INT8 models. On the NPU, the INT8 model infers in 0.46 ms on NSL-KDD and 0.29 ms on UNSW-NB15 (100% NPU execution), 2.7–4.2× faster than the same model on the Cortex-M55 CPU, while occupying 120.6–137.7 KB Flash and 0.5–1.25 KB RAM. Tree-based baselines (Random Forest, XGBoost) achieve higher overall accuracy on UNSW-NB15 but cannot be compiled for the NPU at all. To our knowledge, this is the first publicly documented IDS deployment on an ARM Cortex-M NPU and the first publicly documented empirical validation of T=1 SNN–ANN equivalence on commercial NPU silicon.