arXiv cs.AI· Pengkun Wang, Rui Mao, Zhe Zhao, Dongting Xie, Haibin Wen, Song Xu, Di Wu, Xu Wang, Zhengyang Zhou, Binwu Wang, Xiaofeng Cao, Yang Wang·· 2 小时前AI 评分46
ChipOmni：面向电子设计自动化的通用 AI 智能体
ChipOmni: A General-Purpose AI Agent for Electronic Design Automation
AI 导读
研究者提出 ChipOmni，一个面向电子设计自动化（EDA）的知识增强、上下文演化的 AI 智能体框架，整合多模态理解、动态规划、多智能体协作与验证驱动的工具交互。
正文 · 原文
Authors:Pengkun Wang, Rui Mao, Zhe Zhao, Dongting Xie, Haibin Wen, Song Xu, Di Wu, Xu Wang, Zhengyang Zhou, Binwu Wang, Xiaofeng Cao, Yang Wang
Abstract:Electronic design automation (EDA) workflows remain highly fragmented, requiring engineers to coordinate heterogeneous representations, specialised software tools, and iterative verification across multiple design stages. Although large language models have brought more general reasoning and code-generation capabilities to EDA, many existing approaches remain focused on isolated tasks and struggle to plan, verify, and repair designs autonomously within executable engineering environments. Here we introduce ChipOmni, a knowledge-enhanced, context-evolving AI agent framework for EDA. ChipOmni integrates multimodal understanding, dynamic planning, multi-agent collaboration, and verification-driven tool interaction into a unified autonomous system. By continually incorporating feedback from external engineering tools, the framework maintains an evolving task context, integrates domain knowledge, and iteratively refines intermediate designs. Across benchmarks for analog circuit, RTL, testbench, and formal-assertion generation, ChipOmni improves multiple task-specific measures over the evaluated baselines and solves all 30 AnalogCoder-Pro tasks. In a long-horizon case study, ChipOmni advances a natural-language RISC-V specification through three design levels to a micro-SoC GDSII layout that passes physical sign-off, without human intervention after initialization. Together, these results show that ChipOmni can coordinate verification-driven design across heterogeneous tasks and sustain long-horizon engineering workflows, advancing a shift from fragmented, tool-centred processes towards agent-centred, closed-loop systems.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13020 [cs.AI] |
| (or arXiv:2611.13020v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13020 arXiv-issued DOI via DataCite |
Submission history
From: Pengkun Wang [view email]
[v1]
Mon, 9 Nov 2026 06:28:34 UTC (5,510 KB)
来源：arXiv cs.AI · arxiv.org