An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917967933997056 |
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| author | Li, Tseng-Jen Chang, Tian-Sheuan |
| author_facet | Li, Tseng-Jen Chang, Tian-Sheuan |
| contents | Transformer-based models have become the \textit{de facto} backbone across many fields, such as computer vision and natural language processing. However, as these models scale in size, external memory access (EMA) for weight and activations becomes a critical bottleneck due to its significantly higher energy consumption compared to internal computations. While most prior work has focused on optimizing the self-attention mechanism, little attention has been given to optimizing data transfer during linear projections, where EMA costs are equally important. In this paper, we propose the Tile-based Adaptive Stationary (TAS) scheme that selects the input or weight stationary in a tile granularity, based on the input sequence length. Our experimental results demonstrate that TAS can significantly reduce EMA by more than 97\% compared to traditional stationary schemes, while being compatible with various attention optimization techniques and hardware accelerators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators Li, Tseng-Jen Chang, Tian-Sheuan Machine Learning Hardware Architecture Transformer-based models have become the \textit{de facto} backbone across many fields, such as computer vision and natural language processing. However, as these models scale in size, external memory access (EMA) for weight and activations becomes a critical bottleneck due to its significantly higher energy consumption compared to internal computations. While most prior work has focused on optimizing the self-attention mechanism, little attention has been given to optimizing data transfer during linear projections, where EMA costs are equally important. In this paper, we propose the Tile-based Adaptive Stationary (TAS) scheme that selects the input or weight stationary in a tile granularity, based on the input sequence length. Our experimental results demonstrate that TAS can significantly reduce EMA by more than 97\% compared to traditional stationary schemes, while being compatible with various attention optimization techniques and hardware accelerators. |
| title | An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2503.19640 |