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EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD

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arxiv 2405.06676 v1 pith:OZGDEU63 submitted 2024-05-04 cs.CL cs.AIcs.AR

Bing-Yue Wu , Utsav Sharma , Sai Rahul Dhanvi Kankipati , Ajay Yadav , Bintu Kappil George , Sai Ritish Guntupalli , Austin Rovinski , Vidya A. Chhabria This is my paper
classification cs.CLcs.AIcs.AR
keywords datasetdesignopenroadavailablecompriseddatalanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A new multimodal benchmark of 3,614 circuit QA pairs shows that large language models perform worst on back-end layout and computation tasks, and that current models generally underperform on circuit design questions.

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