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

• URL: http://arxiv.org/abs/2506.08332v2
• Date: Fri, 01 Aug 2025 06:36:42 GMT
• Title: ORFS-agent: Tool-Using Agents for Chip Design Optimization
• Authors: Amur Ghose, Andrew B. Kahng, Sayak Kundu, Zhiang Wang,
• Abstract summary: Large Language Models (LLMs) offer new opportunities for learning and reasoning within such high-dimensional optimization tasks.<n>We introduce ORFS-agent, an LLM-based iterative optimization agent that automates parameter tuning in an open-source hardware design flow.<n>Our empirical evaluations on two different technology nodes and a range of circuit benchmarks indicate that ORFS-agent can improve both routed wirelength and effective clock period by over 13%.
• Score: 0.8088986164437757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In the context of integrated circuit design, modern flows (e.g., going from a register-transfer level netlist to physical layouts) involve extensive configuration via thousands of parameters, and small changes to these parameters can have large downstream impacts on desired outcomes - namely 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 clear improvements over standard Bayesian optimization approaches in terms of resource efficiency and final design metrics. Our empirical evaluations on two different technology nodes and a range of circuit benchmarks indicate that ORFS-agent can improve both routed wirelength and effective clock period by over 13%, all while using 40% fewer optimization iterations. Moreover, by following natural language objectives to trade off certain metrics for others, ORFS-agent demonstrates a flexible and interpretable framework for multi-objective optimization. Crucially, RFS-agent is modular and model-agnostic, and can be plugged in to any frontier LLM without any further fine-tuning.

Related papers

• Scaling Intelligence: Designing Data Centers for Next-Gen Language Models [0.13332839594069593]
  Large Language Models (LLMs) demand a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness.<n>Our work provides a comprehensive co-design framework that jointly explores FLOPS, bandwidth and capacity, multiple network topologies, and popular parallelism/optimization strategies.<n>Our findings offer actionable insights and a practical roadmap for designing AI data centers.
  arXiv  Detail & Related papers  (2025-06-17T22:29:37Z)
• syftr: Pareto-Optimal Generative AI [40.80352098169579]
  syftr is a framework that performs efficient multi-objective search over a broad space of agentic and non-agentic RAG configurations.<n>Syftr finds flows which are on average approximately 9 times cheaper while preserving most of the accuracy of the most accurate flows.
  arXiv  Detail & Related papers  (2025-05-26T17:43:13Z)
• Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? [42.362388367152256]
  Large language models (LLMs) are used to fine-tune a parameter-efficient version of Code Llama using LoRA.<n>Our method achieves competitive or superior results in terms of Root Mean Square Error (RMSE) while significantly reducing computational overhead.
  arXiv  Detail & Related papers  (2025-04-08T13:15:47Z)
• A Survey on the Optimization of Large Language Model-based Agents [16.733092886211097]
  Large Language Models (LLMs) have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks.<n>However, current work typically relies on prompt design or fine-tuning strategies applied to vanilla LLMs.<n>We provide a comprehensive review of LLM-based agent optimization approaches, categorizing them into parameter-driven and parameter-free methods.
  arXiv  Detail & Related papers  (2025-03-16T10:09:10Z)
• IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts [40.98057887166546]
  Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning.<n>We introduce Iterative Refinement, a novel strategy for LLM-driven ML pipeline design inspired by how human ML experts iteratively refine models.<n>By systematically updating individual components based on real training feedback, Iterative Refinement improves overall model performance.
  arXiv  Detail & Related papers  (2025-02-25T01:52:37Z)
• Using Large Language Models for Parametric Shape Optimization [2.464331481632096]
  We develop an optimization framework, LLM-PSO, to determine the optimal shape of parameterized engineering designs.<n>Our preliminary exploration may inspire further investigations into harnessing LLMs for shape optimization and engineering design more broadly.
  arXiv  Detail & Related papers  (2024-12-11T03:35:38Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.<n>Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.<n>We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.<n>We identify two pivotal factors in model parameter learning: update direction and update method.<n>We develop a capable Gradient-inspired Prompt-based GPO.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning [77.61565726647784]
  Motivated by advances in neural architecture search, we propose AutoPEFT for automatic PEFT configuration selection. We show that AutoPEFT-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT without incurring substantial training efficiency costs.
  arXiv  Detail & Related papers  (2023-01-28T08:51:23Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.