Machine Learning Framework for Early Power, Performance, and Area Estimation of RTL
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916627150274560 |
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| author | Chattopadhyay, Anindita Sutrakar, Vijay Kumar |
| author_facet | Chattopadhyay, Anindita Sutrakar, Vijay Kumar |
| contents | A critical stage in the evolving landscape of VLSI design is the design phase that is transformed into register-transfer level (RTL), which specifies system functionality through hardware description languages like Verilog. Generally, evaluating the quality of an RTL design demands full synthesis via electronic design automation (EDA) tool is time-consuming process that is not well-suited to rapid design iteration and optimization. Although recent breakthroughs in machine Learning (ML) have brought early prediction models, these methods usually do not provide robust and generalizable solutions with respect to a wide range of RTL designs. This paper proposes a pre-synthesis framework that makes early estimation of power, performance and area (PPA) metrics directly from the hardware description language (HDL) code making direct use of library files instead of toggle files. The proposed framework introduces a bit-level representation referred to as the simple operator graph (SOG), which uses single-bit operators to generate a generalized and flexible structure that closely mirrors the characteristics of post synthesis design. The proposed model bridges the RTL and post-synthesis design, which will help in precisely predicting key metrics. The proposed tree-based ML framework shows superior predictive performance PPA estimation. Validation is carried out on 147 distinct RTL designs. The proposed model with 147 different designs shows accuracy of 98%, 98%, and 90% for WNS, TNS and power, respectively, indicates significant accuracy improvements relative to state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Framework for Early Power, Performance, and Area Estimation of RTL Chattopadhyay, Anindita Sutrakar, Vijay Kumar Artificial Intelligence Machine Learning A critical stage in the evolving landscape of VLSI design is the design phase that is transformed into register-transfer level (RTL), which specifies system functionality through hardware description languages like Verilog. Generally, evaluating the quality of an RTL design demands full synthesis via electronic design automation (EDA) tool is time-consuming process that is not well-suited to rapid design iteration and optimization. Although recent breakthroughs in machine Learning (ML) have brought early prediction models, these methods usually do not provide robust and generalizable solutions with respect to a wide range of RTL designs. This paper proposes a pre-synthesis framework that makes early estimation of power, performance and area (PPA) metrics directly from the hardware description language (HDL) code making direct use of library files instead of toggle files. The proposed framework introduces a bit-level representation referred to as the simple operator graph (SOG), which uses single-bit operators to generate a generalized and flexible structure that closely mirrors the characteristics of post synthesis design. The proposed model bridges the RTL and post-synthesis design, which will help in precisely predicting key metrics. The proposed tree-based ML framework shows superior predictive performance PPA estimation. Validation is carried out on 147 distinct RTL designs. The proposed model with 147 different designs shows accuracy of 98%, 98%, and 90% for WNS, TNS and power, respectively, indicates significant accuracy improvements relative to state-of-the-art methods. |
| title | Machine Learning Framework for Early Power, Performance, and Area Estimation of RTL |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.16203 |