AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents

2025-08-01

AI Summary

To address strong script dependencies, high tool coupling in RTL-to-GDSII flows, the need for costly LLM fine-tuning, and the absence of a unified evaluation framework, this paper proposes a microservice-based LLM agent architecture. First, it introduces the Model Context Protocol to enable parallel context learning. Second, it replaces fine-tuning with structured prompt engineering to support natural-language-driven task decomposition and TCL parameter extraction. Third, it extends the CodeBLEU metric to quantitatively assess generated script quality. Evaluated on five EDA benchmarks, the approach achieves significant improvements: +28.6% average automation accuracy and 3.2× average speedup in execution time; generated scripts exhibit higher functional correctness and maintainability than baselines. The implementation is open-sourced to ensure reproducibility and foster community collaboration.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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

Problem

• Automating EDA workflows to reduce manual scripting
• Standardizing LLM integration for scalable RTL-to-GDSII flow
• Improving TCL script quality with structured prompt engineering

Innovation

• Microservice-based LLM agents for EDA automation
• Model Context Protocol for standardized natural language
• Structured prompt engineering limits fine-tuning needs