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.

翻译：开源电子设计自动化（EDA）工具正在通过解决商用EDA工具在复杂性、成本与可及性等方面的关键壁垒，迅速改变芯片设计格局。近期大规模语言模型（LLM）的进展进一步提升了芯片设计效率，其能够在工具配置、设计决策与流程自动化等多种任务中为用户提供辅助。本文提出ORAssistant——一个基于检索增强生成（RAG）技术、面向OpenROAD平台的对话助手。该助手旨在通过以自然文本形式针对常见用户问题（包括安装、命令使用、流程配置与执行等）提供上下文相关的应答，从而提升用户在RTL至GDSII全流程中的使用体验。目前，ORAssistant已集成OpenROAD、OpenROAD-flow-scripts、Yosys、OpenSTA及KLayout等工具，其数据模型构建于公开文档与GitHub资源之上。所提出的架构具备可扩展性，支持扩展到其他开源工具、操作模式及LLM模型。我们采用Google Gemini作为基础LLM模型来构建与测试ORAssistant。基于RAG的模型初步评估结果显示，相较于未经微调的LLM，其在性能与准确性方面均有显著提升。