Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 26 references
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.
The results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring and support autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor.
Steven C. Nesbit, Victor M. Vergara, Michael A. Felix et al.· 0 citations
An in depth analysis of the different trade-offs between quantization, generalization performance, and energy efficiency between binary SNNs, multi-level SNNs and ANNs for two different applications scenarios: image classification and image denoising and results show that multi-level spiking neurons provide better information compression, allowing therefore a reduction in latency without performance loss for classification tasks.
A. Castagnetti, Alain Pegatoquet, Benoît Miramond· IEEE Journal on Selected Are...· 0 citations
Recent advances in biologically inspired neural computation have sparked increasing interest in developing hardware-efficient architectures capable of emulating brain-like cognitive abilities like low power consumption and less inference latency. However, significant hardware overhead and spike-processing complexity remain major challenges in FPGA implementations of spiking neural networks. In this work, we propose a sparse spike-aware and weight pruning FPGA architecture based on LIF neurons that minimizes spike activity and synaptic operations through pruning-aware event-driven computation. The proposed sparse spike-aware SNN architecture was evaluated using the Iris dataset. The dataset was divided into 80% training and 20% testing samples. Pre-trained weights obtained from software-level training were deployed onto the FPGA-based LIF classifier. Classification accuracy was computed by comparing predicted output spikes against ground-truth class labels. Experimental results demonstrated that the proposed architecture achieved an overall classification accuracy of 93.3% while maintaining low hardware resource utilization and reduced power consumption. Moreover, the implementation achieves superior energy efficiency, consuming only 3.5 W total on-chip power and utilizing 587 logic cells, confirming its suitability for compact, real-time edge computing neuromorphic applications.
Alishba Masood, M. Khurram· Journal of Low Power Electro...· 0 citations
Embedded fault diagnosis in three-phase inverters must satisfy the sub-watt power budget of converter control hardware, but conventional convolutional neural network (CNN)-based methods require dense multiply-accumulate operations and impose substantial inference energy. This work proposes an event-driven neuromorphic framework for energy-efficient open-circuit (OC) fault diagnosis. A CNN trained on current-vector trajectory matrices is converted into a spiking neural network (SNN) and evaluated using the NengoLoihi framework with Loihi-based neuromorphic energy estimation. By exploiting the sparse structure of trajectory matrices, the SNN activates computation only in informative regions instead of processing the full feature map densely. Experiments on a three-phase inverter platform show that the proposed method achieves 11 microjoules per diagnosis, corresponding to a 382 times inference-energy reduction compared with a GPU-based CNN, while maintaining 100% diagnostic accuracy. Robustness is further validated under unbalanced loading, current amplitude step changes, and injected measurement noise.
This work introduces the first end-to-end neuromorphic spike-encoding and evaluation of the TIMIT dataset and quantifies the pipeline's efficiency with hardware-agnostic metrics based on the quantitative spiking activity.
Valentin Meunier, Amélie Gruel, Pierre Lewden et al.· 0 citations
A systematic methodology to convert a trained DWSNN into an equivalent Spiking Neural P (SN P) system, a biologically-inspired, rule-based computational model drawn from membrane computing, by extracting symbolic firing rules from the hidden-layer spike activity is proposed.
Pietro Savazzi, Mauro Marchese, A. Vizziello et al.· 0 citations
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