A survey on FPGA-based accelerator for ML models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913621032828928 |
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| author | Yan, Feng Koch, Andreas Sinnen, Oliver |
| author_facet | Yan, Feng Koch, Andreas Sinnen, Oliver |
| contents | This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81\%) compared to training acceleration (13\%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15666 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A survey on FPGA-based accelerator for ML models Yan, Feng Koch, Andreas Sinnen, Oliver Hardware Architecture Machine Learning 68W25, 68M20, 68Q25 I.5.4; C.1.3; B.5.2 This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81\%) compared to training acceleration (13\%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications. |
| title | A survey on FPGA-based accelerator for ML models |
| topic | Hardware Architecture Machine Learning 68W25, 68M20, 68Q25 I.5.4; C.1.3; B.5.2 |
| url | https://arxiv.org/abs/2412.15666 |