Neuromorphic Spiking Architectures for Low-Power Real-Time Anomaly Detection

Abstract

Anomaly detection on battery-powered, always-on edge devices poses a structural mismatch for conventional deep learning:
dense, clock-driven networks spend roughly the same energy per cycle whether or not the input contains anything worth
reporting, which is a poor fit for signals that are quiet for long stretches and occasionally, briefly, informative. Spiking neural
networks and the neuromorphic hardware built to run them offer an event-driven alternative in which computation and
communication are triggered by sparse spikes rather than a global clock, so idle input costs close to nothing. This article
synthesizes the literature spanning the computational foundations of spiking neurons and spike-timing-dependent plasticity,
the major neuromorphic hardware platforms (TrueNorth, Loihi, SpiNNaker) and their directly measured energy advantages
over conventional processors, the surrogate-gradient and ANN-to-SNN conversion techniques that now make deep spiking
networks trainable at scale, and a growing applied literature that uses these architectures for real-time anomaly detection in
machine vibration, acoustic fault monitoring, network intrusion detection, and event-camera-based vision. Read together, the
evidence supports spiking neuromorphic architectures as a genuinely promising and, in several documented cases, already
demonstrated approach to low-power real-time anomaly detection, while leaving standardized cross-domain benchmarking,
joint accuracy-energy tradeoff characterization, and on-device continual adaptation as an open research agenda.