12 citations · 29 across the 9 of their papers we have counts for
6 papers · 1 filter
Parallel Time-Aligned Spiking Self-Attention for Consistent Integer-Valued Training and Spike-Driven Inference
Peng Xue, Wei Fang, Kaiwei Che +4
Integer-valued leaky integrate-and-fire (I-LIF) neurons and spike firing approximation (SFA) reduce temporal training cost by representing spike trains as firing counts and normali…
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…
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…
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…
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
Zhaokun Zhou, Yijie Lu, Kaiwei Che +7
Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-attention mechanism, which is the cornersto…
Auto-Spikformer: Spikformer Architecture Search
Kaiwei Che, Zhaokun Zhou, Zhengyu Ma +5
The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their b…