7 citations · 12 across the 18 of their papers we have counts for
9 papers · 1 filter
SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
Kai Sun, Peibo Duan, Zhanglu Yan +3
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synapti…
A Time-Variant Spiking Neuron from a Unified State-Dynamics Perspective
Jing Yang, Qianhui Liu, Gang Pan +1
Spiking neural networks (SNNs) have increasingly incorporated rich neuronal dynamics to enhance temporal processing. However, richer dynamics do not necessarily enable neurons to a…
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
Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely detection…
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…
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
Kaiwen Tang, Zhanglu Yan, Weng-Fai Wong
For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devic…
Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives
Zhanglu Yan, Zhenyu Bai, Kaiwen Tang +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…
Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks
Zhanglu Yan, Jun Zhou, Weng-Fai Wong
Spiking neural networks (SNNs) are well suited for resource-constrained applications as they do not need expensive multipliers. In a typical rate-encoded SNN, a series of binary sp…