AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents
This work addresses scalability and efficiency issues for EDA engineers by providing an incremental automation framework that reduces the need for expensive fine-tuning.
The paper tackles the problem of manual scripting and tool-specific interactions in Electronic Design Automation (EDA) workflows by introducing AutoEDA, a framework that uses LLM agents with structured prompt engineering to automate the RTL-to-GDSII flow, resulting in improved automation accuracy, efficiency, and script quality compared to existing methods.
Modern Electronic Design Automation (EDA) workflows, especially the RTL-to-GDSII flow, require heavily manual scripting and demonstrate a multitude of tool-specific interactions which limits scalability and efficiency. While LLMs introduces strides for automation, existing LLM solutions require expensive fine-tuning and do not contain standardized frameworks for integration and evaluation. We introduce AutoEDA, a framework for EDA automation that leverages paralleled learning through the Model Context Protocol (MCP) specific for standardized and scalable natural language experience across the entire RTL-to-GDSII flow. AutoEDA limits fine-tuning through structured prompt engineering, implements intelligent parameter extraction and task decomposition, and provides an extended CodeBLEU metric to evaluate the quality of TCL scripts. Results from experiments over five previously curated benchmarks show improvements in automation accuracy and efficiency, as well as script quality when compared to existing methods. AutoEDA is released open-sourced to support reproducibility and the EDA community. Available at: https://github.com/AndyLu666/MCP-EDA-Server