DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910617900679168 |
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| author | Xu, Boxun Geng, Hejia Yin, Yuxuan Li, Peng |
| author_facet | Xu, Boxun Geng, Hejia Yin, Yuxuan Li, Peng |
| contents | Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transformers that thrive in ultra-low power operations on neuromorphic hardware, however, without fully unlocking the potential of spiking neural networks. We introduce DS2TA, a Denoising Spiking transformer with attenuated SpatioTemporal Attention, designed specifically for vision applications. DS2TA introduces a new spiking attenuated spatiotemporal attention mechanism that considers input firing correlations occurring in both time and space, thereby fully harnessing the computational power of spiking neurons at the core of the transformer architecture. Importantly, DS2TA facilitates parameter-efficient spatiotemporal attention computation without introducing extra weights. DS2TA employs efficient hashmap-based nonlinear spiking attention denoisers to enhance the robustness and expressive power of spiking attention maps. DS2TA demonstrates state-of-the-art performances on several widely adopted static image and dynamic neuromorphic datasets. Operated over 4 time steps, DS2TA achieves 94.92% top-1 accuracy on CIFAR10 and 77.47% top-1 accuracy on CIFAR100, as well as 79.1% and 94.44% on CIFAR10-DVS and DVS-Gesture using 10 time steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention Xu, Boxun Geng, Hejia Yin, Yuxuan Li, Peng Neural and Evolutionary Computing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transformers that thrive in ultra-low power operations on neuromorphic hardware, however, without fully unlocking the potential of spiking neural networks. We introduce DS2TA, a Denoising Spiking transformer with attenuated SpatioTemporal Attention, designed specifically for vision applications. DS2TA introduces a new spiking attenuated spatiotemporal attention mechanism that considers input firing correlations occurring in both time and space, thereby fully harnessing the computational power of spiking neurons at the core of the transformer architecture. Importantly, DS2TA facilitates parameter-efficient spatiotemporal attention computation without introducing extra weights. DS2TA employs efficient hashmap-based nonlinear spiking attention denoisers to enhance the robustness and expressive power of spiking attention maps. DS2TA demonstrates state-of-the-art performances on several widely adopted static image and dynamic neuromorphic datasets. Operated over 4 time steps, DS2TA achieves 94.92% top-1 accuracy on CIFAR10 and 77.47% top-1 accuracy on CIFAR100, as well as 79.1% and 94.44% on CIFAR10-DVS and DVS-Gesture using 10 time steps. |
| title | DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention |
| topic | Neural and Evolutionary Computing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.15375 |