Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation

• URL: http://arxiv.org/abs/2502.10857v1
• Date: Sat, 15 Feb 2025 16:59:29 GMT
• Title: Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation
• Authors: Haoyuan Wu, Haisheng Zheng, Zhuolun He, Bei Yu,
• Abstract summary: We introduce EDAid, a multi-agent collaboration system where multiple agents harboring divergent thoughts converge towards a common goal. Specifically, each agent is controlled by ChipLlama models, which are expert LLMs fine-tuned for EDA flow automation. Our experiments demonstrate the state-of-the-art (SOTA) performance of our ChipLlama models and validate the effectiveness of our EDAid.
• Score: 5.536630285985836
• License:
• Abstract: Recently, with the development of tool-calling capabilities in large language models (LLMs), these models have demonstrated significant potential for automating electronic design automation (EDA) flows by interacting with EDA tool APIs via EDA scripts. However, considering the limited understanding of EDA tools, LLMs face challenges in practical scenarios where diverse interfaces of EDA tools exist across different platforms. Additionally, EDA flow automation often involves intricate, long-chain tool-calling processes, increasing the likelihood of errors in intermediate steps. Any errors will lead to the instability and failure of EDA flow automation. To address these challenges, we introduce EDAid, a multi-agent collaboration system where multiple agents harboring divergent thoughts converge towards a common goal, ensuring reliable and successful EDA flow automation. Specifically, each agent is controlled by ChipLlama models, which are expert LLMs fine-tuned for EDA flow automation. Our experiments demonstrate the state-of-the-art (SOTA) performance of our ChipLlama models and validate the effectiveness of our EDAid in the automation of complex EDA flows, showcasing superior performance compared to single-agent systems.

Related papers

• A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops [3.729242965449096]
  This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries. The framework achieves optimal performance without human input by autonomously generating and testing hypotheses. Case studies show significant improvements in output quality, relevance, and actionability.
  arXiv  Detail & Related papers  (2024-12-22T20:08:04Z)
• Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job? [0.0]
  SelfScore is a benchmark designed to assess the performance of automated Large Language Model (LLM) agents on help desk and professional consultation tasks. The benchmark evaluates agents on problem complexity and response helpfulness, ensuring transparency and simplicity in its scoring system. The study raises concerns about the potential displacement of human workers, especially in areas where AI technologies excel.
  arXiv  Detail & Related papers  (2024-10-05T14:37:35Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline. Recent works have started exploiting large language models (LLM) to lessen such burden. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
  AutoAct is an automatic agent learning framework for QA. It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
  arXiv  Detail & Related papers  (2024-01-10T16:57:24Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
  Large Language Models (LLMs) have emerged human-like intelligence. This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation. We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
  arXiv  Detail & Related papers  (2023-11-02T14:32:16Z)
• ChatEDA: A Large Language Model Powered Autonomous Agent for EDA [6.858976599086164]
  This research paper introduces ChatEDA, an autonomous agent for EDA empowered by an LLM, AutoMage, and EDA tools serving as executors. ChatEDA streamlines the design flow from the Register-Transfer Level (RTL) to the Graphic Data System Version II (GDSII) by effectively managing task decomposition, script generation, and task execution.
  arXiv  Detail & Related papers  (2023-08-20T08:32:13Z)
• OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System [85.8338446357469]
  We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques. We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
  arXiv  Detail & Related papers  (2023-03-01T13:35:22Z)
• Machine Learning for Electronic Design Automation: A Survey [23.803190584543863]
  With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. The recent breakthrough of machine learning (ML) and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks.
  arXiv  Detail & Related papers  (2021-01-10T12:54:37Z)

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.