From the 1 of 9 linked papers with an AI index.
9 papers
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…
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…
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.…
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…
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…
SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting
Kaiwen Tang, Jiaqi Zheng, Yuze Jin +4
Time-series forecasting in domains like traffic management and industrial monitoring often requires real-time, energy-efficient processing on edge devices with limited resources. S…