Ultra-low-power Anomaly Detection at the Edge with Spiking Neural Networks

Abstract

Deploying anomaly detection models on energy constrained industrial platforms is an open and pivotal challenge for manufacturing. This paper presents ALPS (Anomaly detection with Low Power Spiking neural networks), a lightweight neuro-morphic pipeline for acoustic fault classification in a reciprocating air compressor. Raw audio waveforms are decomposed by a 16-channel band pass filter bank and converted into spike trains, which are then processed by a feed-forward spiking neural network with one hidden layer composed of 128 leaky integrate-and-fire neurons. The full pipeline is deployed on the SynSense Xylo Audio 3 neuromorphic processor after 7-bit post-training quantization. On an eight-class benchmark dataset, the system achieves 0.93 macro F1 while consuming only 3.4 mW, roughly three orders of magnitude less than a 1D convolutional neural network running on a Raspberry Pi 4 at comparable accuracy. A robustness study with structured additive factory noise shows graceful performance degradation and no abrupt collapse. These results demonstrate that neuromorphic hardware is a viable, ultra-low-power alternative for always-on acoustic anomaly detection at the edge.