4 papers
Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks
Dewei Bai, Hongxiang Peng, Yunyun Zeng +2
Spiking Neural Networks (SNNs) are widely regarded as an energy-efficient paradigm for modeling and processing temporal and event-driven information. Incorporating delays in SNNs h…
Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networks
Dewei Bai, Hongxiang Peng, Jiajun Mei +4
Binary spikes provide only two neuronal output states per timestep, limiting the response capacity of spiking neural networks (SNNs) under short simulation horizons. Burst neurons…
Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models
Dewei Bai, Hongxiang Peng, Yunyun Zeng +3
Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token interactions remain fundamentall…
BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning
Hongxiang Peng, Dewei Bai, Hong Qu
Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restr…