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[2508.01012] AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents
Summary
The article presents AutoEDA, a framework that utilizes microservice-based LLM agents to automate Electronic Design Automation (EDA) processes, enhancing efficiency and accuracy in chip design workflows.
Why It Matters
AutoEDA addresses the challenges of traditional EDA methods, which are often labor-intensive and error-prone. By leveraging large language models for automation, it offers a significant advancement in the field, potentially transforming how chip designs are managed and executed. This innovation could lead to more efficient design processes and improved scalability in complex projects.
Key Takeaways
- AutoEDA automates EDA workflows using microservice-based LLM agents.
- The framework improves accuracy by up to 9.9 times compared to traditional methods.
- It reduces token usage by approximately 97% compared to in-context learning.
- MCP-based servers facilitate robust interaction between LLM agents and EDA tools.
- The framework includes a benchmark generation pipeline for diverse EDA scenarios.
Computer Science > Artificial Intelligence arXiv:2508.01012 (cs) [Submitted on 1 Aug 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents Authors:Yiyi Lu, Hoi Ian Au, Junyao Zhang, Jingyu Pan, Guanglei Zhou, Yiting Wang, Jingwei Sun, Ang Li, Jianyi Zhang, Hai Li, Yiran Chen View a PDF of the paper titled AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents, by Yiyi Lu and 10 other authors View PDF HTML (experimental) Abstract:Electronic Design Automation (EDA) remains heavily reliant on tool command language (Tcl) scripting to drive complex RTL-to-GDSII flows. This scripting-based paradigm is labor-intensive, error-prone, and difficult to scale across large design projects. Recent advances in large language models (LLMs) suggest a new paradigm of natural language-driven automation. However, existing EDA efforts remain limited and face key challenges, including the absence of standardized interaction protocols and dependence on external APIs that introduce privacy risks. We present AutoEDA, a framework that leverages the Model Context Protocol (MCP) to enable end-to-end natural language control of RTL-to-GDSII design flows. AutoEDA introduces MCP-based servers for task decomposition, tool selection, and automated error handling, ensuring robust interaction between LLM agents and EDA tools. To enhance reliability and confidentiality, we integrate locally fine-tuned LLM...