English(EN) Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention

LSFormer通过新的注意力机制推动脉冲神经网络发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-12 02:08

研究人员开发了一种新颖的基于Transformer的脉冲神经网络，称为LSFormer，旨在克服现有模型的局限性。LSFormer引入了脉冲响应池化（SPooling）和局部结构感知脉冲自注意力（LS-SSA），以更好地保留区域特征并减少计算冗余。这种新架构利用局部扩张窗口机制来捕捉细粒度细节和更广泛的依赖关系，在Tiny-ImageNet和N-CALTECH101等数据集上取得了最先进的成果。 AI

影响引入了一种更高效、更准确的脉冲神经网络架构，有可能在能源受限的应用中实现更广泛的应用。

排序理由该集群包含一篇详细介绍脉冲神经网络新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Qiang Yu · 2026-05-12 02:08

使用局部结构感知自注意力打破基于Transformer的脉冲神经网络中的全局自注意力瓶颈

Transformer-based Spiking Neural Networks (SNNs) integrate SNNs with global self-attention and have demonstrated impressive performance. However, existing Transformer-based SNNs suffer from two fundamental limitations. First, they typically employ max pooling layers to reduce the…

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使用局部结构感知自注意力打破基于Transformer的脉冲神经网络中的全局自注意力瓶颈

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