ORFS-agent: Tool-Using Agents for Chip Design Optimization

Abstract

Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In integrated circuit design, modern flows (e.g., register-transfer level to physical layout) involve extensive configuration via thousands of parameters, and small changes can have large downstream impacts on design performance, power, and area. Recent advances in Large Language Models (LLMs) offer new opportunities for learning and reasoning within such high-dimensional optimization tasks. In this work, we introduce ORFS-agent, an LLM-based iterative optimization agent that automates parameter tuning in an open-source hardware design flow. ORFS-agent adaptively explores parameter configurations, demonstrating improvements over standard Bayesian optimization approaches in terms of resource efficiency and final design metrics. Across six benchmarks on ASAP7 and SKY130HD, thinking-model backends (Sonnet 4.6 [69] and Kimi K2.5 [28]) improve the geometric-mean normalized wirelength, effective clock period, and co-optimization objectives by up to 1.0%, 1.3%, and 2.7% over OR-AutoTuner while using 40% fewer iterations; the open-weight Kimi K2.5 remains within 0.24% of Sonnet 4.6, enabling private deployment. Relative to the earlier Sonnet 3.5 backend, these thinking models improve the same objectives by up to 7.5%, 3.1%, and 4.0%. Optional retrieval tools accelerate early convergence but do not improve final endpoints. By following natural language objectives to trade off certain metrics for others, ORFS-agent demonstrates a flexible and interpretable framework for multi-objective and constrained optimization. Crucially, ORFS-agent is modular and model-agnostic, and can be plugged into any frontier LLM without any further fine-tuning. We also report checkpoint-aligned trajectories and reasoning summaries that document the agent's decision process.