2 citations · 7 across the 10 of their papers we have counts for
12 papers
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…
SNN: Sub-bit Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jieyuan Zhang +8
Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite re…
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…
QP-SNN: Quantized and Pruned Spiking Neural Networks
Wenjie Wei, Malu Zhang, Zijian Zhou +6
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient pa…
Quantized Spike-driven Transformer
Xuerui Qiu, Malu Zhang, Jieyuan Zhang +7
Spiking neural networks are emerging as a promising energy-efficient alternative to traditional artificial neural networks due to their spike-driven paradigm. However, recent resea…
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
Yimeng Shan, Malu Zhang, Rui-jie Zhu +3
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience res…
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
Yimeng Shan, Xuerui Qiu, Rui-jie Zhu +3
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven o…