EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929339554070528 |
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| author | Wu, Bing-Yue Sharma, Utsav Kankipati, Sai Rahul Dhanvi Yadav, Ajay George, Bintu Kappil Guntupalli, Sai Ritish Rovinski, Austin Chhabria, Vidya A. |
| author_facet | Wu, Bing-Yue Sharma, Utsav Kankipati, Sai Rahul Dhanvi Yadav, Ajay George, Bintu Kappil Guntupalli, Sai Ritish Rovinski, Austin Chhabria, Vidya A. |
| contents | Large language models (LLMs) serve as powerful tools for design, providing capabilities for both task automation and design assistance. Recent advancements have shown tremendous potential for facilitating LLM integration into the chip design process; however, many of these works rely on data that are not publicly available and/or not permissively licensed for use in LLM training and distribution. In this paper, we present a solution aimed at bridging this gap by introducing an open-source dataset tailored for OpenROAD, a widely adopted open-source EDA toolchain. The dataset features over 1000 data points and is structured in two formats: (i) a pairwise set comprised of question prompts with prose answers, and (ii) a pairwise set comprised of code prompts and their corresponding OpenROAD scripts. By providing this dataset, we aim to facilitate LLM-focused research within the EDA domain. The dataset is available at https://github.com/OpenROAD-Assistant/EDA-Corpus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD Wu, Bing-Yue Sharma, Utsav Kankipati, Sai Rahul Dhanvi Yadav, Ajay George, Bintu Kappil Guntupalli, Sai Ritish Rovinski, Austin Chhabria, Vidya A. Computation and Language Artificial Intelligence Hardware Architecture Large language models (LLMs) serve as powerful tools for design, providing capabilities for both task automation and design assistance. Recent advancements have shown tremendous potential for facilitating LLM integration into the chip design process; however, many of these works rely on data that are not publicly available and/or not permissively licensed for use in LLM training and distribution. In this paper, we present a solution aimed at bridging this gap by introducing an open-source dataset tailored for OpenROAD, a widely adopted open-source EDA toolchain. The dataset features over 1000 data points and is structured in two formats: (i) a pairwise set comprised of question prompts with prose answers, and (ii) a pairwise set comprised of code prompts and their corresponding OpenROAD scripts. By providing this dataset, we aim to facilitate LLM-focused research within the EDA domain. The dataset is available at https://github.com/OpenROAD-Assistant/EDA-Corpus. |
| title | EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD |
| topic | Computation and Language Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2405.06676 |