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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.06351 |
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| _version_ | 1866916647167590400 |
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| author | Karakchi, Rasha |
| author_facet | Karakchi, Rasha |
| contents | Designing and optimizing FPGA overlays is a complex and time-consuming process, often requiring multiple trial-and-error iterations to determine a suitable configuration. This paper presents an AI-driven approach to optimizing FPGA overlay configurations, specifically focusing on the NAPOLY+ automata processor implemented on the ZCU104 FPGA. By leveraging machine learning techniques, particularly Random Forest regression, we predict the feasibility and efficiency of different configurations before hardware compilation. Our method significantly reduces the number of required iterations by estimating resource utilization, including logical elements, distributed memory, and fanout, based on historical design data. Experimental results demonstrate that our model achieves high prediction accuracy, closely matching actual resource usage while accelerating the design process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Optimization of Hardware Overlay Configurations Karakchi, Rasha Machine Learning Designing and optimizing FPGA overlays is a complex and time-consuming process, often requiring multiple trial-and-error iterations to determine a suitable configuration. This paper presents an AI-driven approach to optimizing FPGA overlay configurations, specifically focusing on the NAPOLY+ automata processor implemented on the ZCU104 FPGA. By leveraging machine learning techniques, particularly Random Forest regression, we predict the feasibility and efficiency of different configurations before hardware compilation. Our method significantly reduces the number of required iterations by estimating resource utilization, including logical elements, distributed memory, and fanout, based on historical design data. Experimental results demonstrate that our model achieves high prediction accuracy, closely matching actual resource usage while accelerating the design process. |
| title | AI-Driven Optimization of Hardware Overlay Configurations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.06351 |