PIMSYN: Synthesizing Processing-in-memory CNN Accelerators
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917599972950016 |
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| author | Li, Wanqian Sun, Xiaotian Wang, Xinyu Wang, Lei Han, Yinhe Chen, Xiaoming |
| author_facet | Li, Wanqian Sun, Xiaotian Wang, Xinyu Wang, Lei Han, Yinhe Chen, Xiaoming |
| contents | Processing-in-memory architectures have been regarded as a promising solution for CNN acceleration. Existing PIM accelerator designs rely heavily on the experience of experts and require significant manual design overhead. Manual design cannot effectively optimize and explore architecture implementations. In this work, we develop an automatic framework PIMSYN for synthesizing PIM-based CNN accelerators, which greatly facilitates architecture design and helps generate energyefficient accelerators. PIMSYN can automatically transform CNN applications into execution workflows and hardware construction of PIM accelerators. To systematically optimize the architecture, we embed an architectural exploration flow into the synthesis framework, providing a more comprehensive design space. Experiments demonstrate that PIMSYN improves the power efficiency by several times compared with existing works. PIMSYN can be obtained from https://github.com/lixixi-jook/PIMSYN-NN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PIMSYN: Synthesizing Processing-in-memory CNN Accelerators Li, Wanqian Sun, Xiaotian Wang, Xinyu Wang, Lei Han, Yinhe Chen, Xiaoming Hardware Architecture Processing-in-memory architectures have been regarded as a promising solution for CNN acceleration. Existing PIM accelerator designs rely heavily on the experience of experts and require significant manual design overhead. Manual design cannot effectively optimize and explore architecture implementations. In this work, we develop an automatic framework PIMSYN for synthesizing PIM-based CNN accelerators, which greatly facilitates architecture design and helps generate energyefficient accelerators. PIMSYN can automatically transform CNN applications into execution workflows and hardware construction of PIM accelerators. To systematically optimize the architecture, we embed an architectural exploration flow into the synthesis framework, providing a more comprehensive design space. Experiments demonstrate that PIMSYN improves the power efficiency by several times compared with existing works. PIMSYN can be obtained from https://github.com/lixixi-jook/PIMSYN-NN. |
| title | PIMSYN: Synthesizing Processing-in-memory CNN Accelerators |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2402.18114 |