ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD

TL;DR

This paper presents ORAssistant, a RAG-based conversational tool that enhances user interaction with open-source EDA tools like OpenROAD by providing context-aware assistance, improving usability and efficiency in chip design workflows.

Contribution

It introduces a scalable, RAG-based conversational assistant for OpenROAD that integrates multiple open-source EDA tools and demonstrates improved performance over standard LLMs.

Findings

Notable improvements in performance and accuracy over non-fine-tuned LLMs.

Supports extensions to other open-source tools and LLM models.

Provides context-specific, prose-format responses to user queries.

Abstract

Open-source Electronic Design Automation (EDA) tools are rapidly transforming chip design by addressing key barriers of commercial EDA tools such as complexity, costs, and access. Recent advancements in Large Language Models (LLMs) have further enhanced efficiency in chip design by providing user assistance across a range of tasks like setup, decision-making, and flow automation. This paper introduces ORAssistant, a conversational assistant for OpenROAD, based on Retrieval-Augmented Generation (RAG). ORAssistant aims to improve the user experience for the OpenROAD flow, from RTL-GDSII by providing context-specific responses to common user queries, including installation, command usage, flow setup, and execution, in prose format. Currently, ORAssistant integrates OpenROAD, OpenROAD-flow-scripts, Yosys, OpenSTA, and KLayout. The data model is built from publicly available documentation and GitHub resources. The proposed architecture is scalable, supporting extensions to other open-source tools, operating modes, and LLM models. We use Google Gemini as the base LLM model to build and test ORAssistant. Early evaluation results of the RAG-based model show notable improvements in performance and accuracy compared to non-fine-tuned LLMs.