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OpenROAD-Assistant: An Open-Source Large Language Model for Physical Design Tasks

Authors: Sharma, Utsav; Wu, Bing-Yue; Kankipati, Sai Rahul Dhanvi; Chhabria, Vidya; Rovinski, Austin;

OpenROAD-Assistant: An Open-Source Large Language Model for Physical Design Tasks

Abstract

This paper presents OpenROAD-Assistant, an open-source chatbot designed for OpenROAD, leveraging only public data to respond to queries in prose or Python script using the OpenROAD APIs. OpenROAD-Assistant uses the foundational Llama3-8B model, enhanced with retrieval-aware fine-tuning (RAFT) for physical design-specific applications. OpenROAD-Assistant, its RAG database, and all related scripts are available on Zinodo and GitHub. It includes the prompt-script and question-answer adaptors and the associated codes to train the models and perform inference. The minimal hardware requirements are 2 CPU cores, 32GB of RAM, and 4 NVIDIA RTX A5500 GPUs for the Script Adaptor or 1 NVIDIA V100 GPU for the QA Adaptor. The software requirements are Python version >= 3.8 but <3.11.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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