用多个有不同思路的AI agent协作，可靠完成复杂电路设计自动化。
Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation
- 多个由专业大模型驱动的AI agent各自提出方案，协同解决电路设计难题。
- 在复杂设计流程中实现98%的成功率，显著优于单个AI agent。
- 适合需要高可靠性电子设计自动化的工程师和研究者使用。
随着大语言模型（LLM）工具调用能力的发展，这些模型通过调用EDA脚本与工具接口，展现出自动化电子设计自动化（EDA）流程的巨大潜力。然而，由于对EDA工具理解有限，且不同平台的工具接口差异大，实际应用中面临挑战。此外，EDA流程常涉及复杂的长链工具调用，中间步骤出错极易导致整个流程失败。为此，我们提出EDAid，一种基于多智能体协作的系统，让多个持有不同思路的智能体共同收敛至同一目标，确保流程稳定可靠。每个智能体由针对EDA流程优化的ChipLlama模型控制，该模型是经过领域微调的专业大模型。实验表明，ChipLlama模型达到当前最优（SOTA）性能，EDAid系统在复杂EDA流程自动化中表现卓越，显著优于单智能体系统。
原文摘要 · Abstract (English)
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.
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