Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring
Steven C. Nesbit (Information SciencesCAI-3Los Alamos National LaboratoryLos AlamosUSA)Victor M. Vergara (AeroVironment Inc.AlbuquerqueUSA)Michael A. Felix (University of New Mexico COSMIAC Research CenterAlbuquerqueUSA)Evan T. Kain (Air Force Research LaboratoryKirtland AFBUSA)Luis R. Garc\'ia Carrillo (Air Force Research LaboratoryKirtland AFBUSA)Gerd J. Kunde (Nuclear and Particle Physics and ApplicationsP-3Los Alamos National LaboratoryLos AlamosUSA)Andrew T. Sornborger (Information SciencesCAI-3Los Alamos National LaboratoryLos AlamosUSA)
Aug 2026
Artificial IntelligenceMachine Learning
Abstract
Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deployment complexity. We demonstrate autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor under clean and noisy conditions. Log-mel features are computed off chip; normalization, autoencoder inference, L1 reconstruction scoring, and thresholding run on chip. In a clean, microphone-position-invariant ToyADMOS ToyCar benchmark, the on-chip model achieves 0.9959 AUC and 0.9785 standardized pAUC at maximum false-positive rate 0.1. In the DCASE 2026 Task 2 ToyCar noisy benchmark, the model achieves source AUC 0.7990, target AUC 0.6466, and pAUC 0.6426, exceeding reported baseline metrics. Power profiling on a 16-chip Loihi 2 VPX system shows real-time throughput with 0.0406$\unicode{x2013}$0.0426 mJ dynamic energy per sample, two orders of magnitude lower than both a CPU and GPU. These results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring.
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Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
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