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Author

Vittorio Fra

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Conference Sep 2026

SNN2MCU: deploying Spiking Neural Networks on commercial microcontrollers using the Neuromorphic Intermediate Representation

Spiking Neural Networks (SNNs) represent a significant advancement in machine learning, enabling efficient computation inspired by biological neural dynamics and making them suitable for power- and resource-constrained Artificial Intelligence of Things (AIoT) applications. However, their full potential is typically ach...

Andrea Pignata, S. del Vecchio, Vittorio Fra et al. · 0 citations
Preprint Sep 2026

Efficient Semantic Understanding from Digital Foveation

A lightweight active-vision pipeline that combines saliency-driven fixation selection, high-resolution foveal observations, low-resolution contextual information, semantic accumulation, and adaptive computation is introduced, suggesting that substantial semantic understanding can emerge from sparse observations when co...

Caterina Caccavella, Vittorio Fra, Andreas Ziegler et al. · 0 citations
Conference Jul 2026

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

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....

F. Aisa, Umberto Albertin, Mauro Martini et al. · 0 citations

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