MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization
Yiting Wang, Wanghao Ye, Yexiao He, Yiran Chen, Gang Qu, Ang Li

TL;DR
MCP4EDA introduces an LLM-controlled, open-source RTL-to-GDSII automation system that optimizes physical design metrics by analyzing real backend data and iteratively refining synthesis scripts through natural language interaction.
Contribution
It is the first system enabling LLMs to control and optimize the entire open-source EDA flow with backend-aware synthesis optimization using real performance metrics.
Findings
Achieved 15-30% improvements in timing closure.
Real backend data guides synthesis parameter tuning.
Demonstrated practical LLM-controlled EDA automation.
Abstract
This paper presents MCP4EDA, the first Model Context Protocol server that enables Large Language Models (LLMs) to control and optimize the complete open-source RTL-to-GDSII design flow through natural language interaction. The system integrates Yosys synthesis, Icarus Verilog simulation, OpenLane place-and-route, GTKWave analysis, and KLayout visualization into a unified LLM-accessible interface, enabling designers to execute complex multi-tool EDA workflows conversationally via AI assistants such as Claude Desktop and Cursor IDE. The principal contribution is a backend-aware synthesis optimization methodology wherein LLMs analyze actual post-layout timing, power, and area metrics from OpenLane results to iteratively refine synthesis TCL scripts, establishing a closed-loop optimization system that bridges the traditional gap between synthesis estimates and physical implementation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
