Reliable, ultra-low-power neuromorphic vision on edge devices requires attention mechanisms that combine accuracy with parallel, memory-efficient execution. Current Spiking Transformers suit neuromorphic data but retain the \(\mathcal {O}(N^2D)\) complexity of standard self-attention and weak temporal modeling, limitin...
Yi-Xing Li, Wen-Hua Hu, Hao-Hui Peng et al.· Proceedings of the Internati...· 0 citations
Reliable, ultra-low-power neuromorphic vision on edge devices requires attention mechanisms that combine accuracy with parallel, memory-efficient execution. Current Spiking Transformers suit neuromorphic data but retain the \(\mathcal {O}(N^2D)\) complexity of standard self-attention and weak temporal modeling, limitin...
Yi-Xing Li, Wen-Hua Hu, Hao-Hui Peng et al.· Proceedings of the Internati...· 0 citations
The Dual-Stage Spiking Swin Transformer (D2S-SwinT), a neuromorphic architecture that integrates the feature representation capability of Swin Transformers with the event-driven efficiency of brain-inspired computation, is proposed, a neuromorphic architecture that integrates the feature representation capability of Sw...
Hong-Jiang Deng, Wen-Hua Hu, Hao-Hui Peng et al.· Proceedings of the Internati...· 0 citations
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