Showing cs.NEShow all
3 papers · 1 filter
cs.NE2026
BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation
Sihang Guo, Chenlin Zhou, Jiaqi Wang +3
Spiking Neural Networks (SNNs) offer promising energy-efficient alternatives to large language models (LLMs) due to their event-driven nature and ultra-low power consumption. Howev…
cs.NE2026
Adaptive Spiking Neurons for Vision and Language Modeling
Chenlin Zhou, Sihang Guo, Jiaqi Wang +5
Regarded as the third generation of neural networks, Spiking Neural Networks (SNNs) have garnered significant traction due to their biological plausibility and energy efficiency. R…
cs.NE2026
Winner-Take-All Spiking Transformer for Language Modeling
Chenlin Zhou, Sihang Guo, Jiaqi Wang +6
Spiking Transformers, which combine the scalability of Transformers with the sparse, energy-efficient property of Spiking Neural Networks (SNNs), have achieved impressive results i…