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20232026
most citedQP-SNN: Quantized and Pruned Spiking Neural Networks

2 citations · 6 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025★ 2 cited

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…

cs.CV2025★ 1 cited

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…

cs.CV2024★ 1 cited

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…

cs.CV2023★ 2 cited

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…