1 citations · 2 across the 10 of their papers we have counts for
13 papers
SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
Jingya Wang, Dehao Zhang, Shuai Wang +3
ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of training large-scale SNNs fr…
Spike-driven Vision-Language-Action Model
Shuai Wang, Malu Zhang, Mingquan Liu +6
Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. However, most existing models re…
Neural Dynamics Self-Attention for Spiking Transformers
Dehao Zhang, Fukai Guo, Shuai Wang +6
Integrating Spiking Neural Networks (SNNs) with Transformer architectures offers a promising pathway to balance energy efficiency and performance, particularly for edge vision appl…
TP-Spikformer: Token Pruned Spiking Transformer
Wenjie Wei, Xiaolong Zhou, Malu Zhang +8
Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due to their event-driven computing paradigm. However, recent advancements in sp…
Robust Spiking Neural Networks Against Adversarial Attacks
Shuai Wang, Malu Zhang, Yulin Jiang +7
Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing due to their bio-plausible and spike-driven characteristics. However, the…
Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling
Dehao Zhang, Malu Zhang, Shuai Wang +6
The explosive growth in sequence length has intensified the demand for effective and efficient long sequence modeling. Benefiting from intrinsic oscillatory membrane dynamics, Reso…
Training-Free ANN-to-SNN Conversion for High-Performance Spiking Transformer
Jingya Wang, Xin Deng, Wenjie Wei +7
Leveraging the event-driven paradigm, Spiking Neural Networks (SNNs) offer a promising approach for energy-efficient Transformer architectures.While ANN-to-SNN conversion avoids th…
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
Yimeng Shan, Zhenbang Ren, Haodi Wu +11
Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through di…
Memory-Free and Parallel Computation for Quantized Spiking Neural Networks
Dehao Zhang, Shuai Wang, Yichen Xiao +4
Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight an…
Spiking Vision Transformer with Saccadic Attention
Shuai Wang, Malu Zhang, Dehao Zhang +7
The combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable fo…
Binary Event-Driven Spiking Transformer
Honglin Cao, Zijian Zhou, Wenjie Wei +6
Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficienc…
Ternary Spike-based Neuromorphic Signal Processing System
Shuai Wang, Dehao Zhang, Ammar Belatreche +5
Deep Neural Networks (DNNs) have been successfully implemented across various signal processing fields, resulting in significant enhancements in performance. However, DNNs generall…
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting
Shuai Wang, Dehao Zhang, Kexin Shi +4
Thanks to Deep Neural Networks (DNNs), the accuracy of Keyword Spotting (KWS) has made substantial progress. However, as KWS systems are usually implemented on edge devices, energy…