This work provides the first systematic study of transferring direct-coded spiking neural networks to event-based representations while aiming to preserve accuracy and reduce energy use.
Spiking deep residual networks
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Vision SmolMamba adds spike-guided spatio-temporal token pruning to a bidirectional spiking state-space backbone, cutting estimated energy by at least 1.5x versus prior spiking Transformers and Spiking Mamba variants on ImageNet-1K and event-based datasets while keeping competitive accuracy.
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
citing papers explorer
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Direct-to-Event Spiking Neural Network Transfer
This work provides the first systematic study of transferring direct-coded spiking neural networks to event-based representations while aiming to preserve accuracy and reduce energy use.
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Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models
Vision SmolMamba adds spike-guided spatio-temporal token pruning to a bidirectional spiking state-space backbone, cutting estimated energy by at least 1.5x versus prior spiking Transformers and Spiking Mamba variants on ImageNet-1K and event-based datasets while keeping competitive accuracy.
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Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.