ViT-LCA: A Neuromorphic Approach for Vision Transformers
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910910166073344 |
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| author | Takaghaj, Sanaz Mahmoodi |
| author_facet | Takaghaj, Sanaz Mahmoodi |
| contents | The recent success of Vision Transformers has generated significant interest in attention mechanisms and transformer architectures. Although existing methods have proposed spiking self-attention mechanisms compatible with spiking neural networks, they often face challenges in effective deployment on current neuromorphic platforms. This paper introduces a novel model that combines vision transformers with the Locally Competitive Algorithm (LCA) to facilitate efficient neuromorphic deployment. Our experiments show that ViT-LCA achieves higher accuracy on ImageNet-1K dataset while consuming significantly less energy than other spiking vision transformer counterparts. Furthermore, ViT-LCA's neuromorphic-friendly design allows for more direct mapping onto current neuromorphic architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ViT-LCA: A Neuromorphic Approach for Vision Transformers Takaghaj, Sanaz Mahmoodi Neural and Evolutionary Computing Emerging Technologies The recent success of Vision Transformers has generated significant interest in attention mechanisms and transformer architectures. Although existing methods have proposed spiking self-attention mechanisms compatible with spiking neural networks, they often face challenges in effective deployment on current neuromorphic platforms. This paper introduces a novel model that combines vision transformers with the Locally Competitive Algorithm (LCA) to facilitate efficient neuromorphic deployment. Our experiments show that ViT-LCA achieves higher accuracy on ImageNet-1K dataset while consuming significantly less energy than other spiking vision transformer counterparts. Furthermore, ViT-LCA's neuromorphic-friendly design allows for more direct mapping onto current neuromorphic architectures. |
| title | ViT-LCA: A Neuromorphic Approach for Vision Transformers |
| topic | Neural and Evolutionary Computing Emerging Technologies |
| url | https://arxiv.org/abs/2411.00140 |