3 papers
cs.NE2026
YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap
Brian Pachideh, Sven Nitzsche, Moritz Neher +5
Spiking Neural Networks (SNNs) promise significant advantages over conventional Artificial Neural Networks (ANNs) for applications requiring real-time processing of temporally spar…
cs.LG2025
Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance
Alexandru Vasilache, Sven Nitzsche, Christian Kneidl +3
Advancements in Industrial Internet of Things (IIoT) sensors enable sophisticated Predictive Maintenance (PM) with high temporal resolution. For cost-efficient solutions, vibration…
cs.LG2025
A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications
Alexandru Vasilache, Jona Scholz, Vincent Schilling +4
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datas…