arXiv AI
Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
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arXiv AI
Aug 11
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
arXiv Machine Learning
Jun 11
A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks
arXiv:2606. 11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients.
arXiv AI
Jun 30
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
arXiv:2605. 08022v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networks (ANNs).
arXiv Machine Learning
Jul 17
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
arXiv AI
Aug 17
SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
arXiv:2608. 13702v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains challenging because the non-differentiable spike function requires surrogate gradients whose fixed shape may be suboptimal across layers and training stages.
arXiv AI
Aug 7
Ge$^\text{2}$mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer
arXiv:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).