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arXiv AI By Steven C. Nesbit (Information Sciences, CAI-3, Los Alamos National Laboratory, Los Alamos, USA), Victor M. Vergara (AeroVironment Inc., Albuquerque, USA), Michael A. Felix (University of New Mexico COSMIAC Research Center, Albuquerque, USA), Evan T. Kain (Air Force Research Laboratory, Kirtland AFB, USA), Luis R. Garc\'ia Carrillo (Air Force Research Laboratory, Kirtland AFB, USA), Gerd J. Kunde (Nuclear and Particle Physics and Applications, P-3, Los Alamos National Laboratory, Los Alamos, USA), Andrew T. Sornborger (Information Sciences, CAI-3, Los Alamos National Laboratory, Los Alamos, USA)

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

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The paper presents an autoencoder-based acoustic anomaly detection system implemented on Intel’s Loihi 2 neuromorphic processor. It achieves high detection performance—0.9959 AUC on a clean ToyADMOS ToyCar benchmark and 0.7990 source AUC on a noisy DCASE 2026 Task 2 ToyCar benchmark—while operating with only 0.0406–0.0426 mJ of dynamic energy per sample, far below CPU and GPU baselines. The system demonstrates that low‑power, on‑chip inference is feasible for persistent machine monitoring.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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