3 papers
cs.NE2026
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
Kaiwei Che, Zhengyu Ma, Yifan Huang +5
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is cr…
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…
cs.NE2024
Spatial-Temporal Search for Spiking Neural Networks
Kaiwei Che, Zhaokun Zhou, Li Yuan +3
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation…