collaborators

13 papers

cs.NE2026

Lapis: Laplacian Spiking Attention via First-Spike Timing and Membrane Leakage

Kaiwen Tang, Jiaqi Zheng, Zixuan Zhu +3

Self-attention has become central to spiking vision transformers, yet its query-key scoring is still largely inherited from dense networks. Existing spiking variants either simplif…

cs.NE2026

Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection

Kaiwen Tang, Jiaqi Dong, Zhanglu Yan +1

The paper proposes an energy‑efficient spiking neural network framework, with a quantization‑compatible training scheme, for detecting muscle fatigue from surface EMG signals, achi…

cs.AI2026

Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer

Zhanglu Yan, Jiayi Mao, Kaiwen Tang +6

Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.…

cs.NE2026

Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives

Zhanglu Yan, Zhenyu Bai, Weng-Fai Wong +1

Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. Ho…

cs.NE2026

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

Kaiwen Tang, Di Yu, Jiaqi Zheng +4

Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (L…

cs.AR2026

SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification

Zhanglu Yan, Zhenyu Bai, Tulika Mitra +1

Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) off…