Summary of Eda Corpus: a Large Language Model Dataset For Enhanced Interaction with Openroad, by Bing-yue Wu et al.
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD
by Bing-Yue Wu, Utsav Sharma, Sai Rahul Dhanvi Kankipati, Ajay Yadav, Bintu Kappil George, Sai Ritish Guntupalli, Austin Rovinski, Vidya A. Chhabria
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an open-source dataset to bridge the gap in using large language models (LLMs) for chip design, particularly with OpenROAD, a widely adopted open-source EDA toolchain. The dataset features over 1000 data points, structured into two formats: pairwise sets of question prompts with prose answers and code prompts with corresponding OpenROAD scripts. This dataset aims to facilitate LLM-focused research within the EDA domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it possible for researchers to use large language models in chip design by providing a publicly available dataset that can be used for training and distribution. The dataset has over 1000 data points, which are very helpful for this type of work. |
