AiEDA: An Open-Source AI-Aided Design Library for Design-to-Vector

• URL: http://arxiv.org/abs/2511.05823v1
• Date: Sat, 08 Nov 2025 03:14:26 GMT
• Title: AiEDA: An Open-Source AI-Aided Design Library for Design-to-Vector
• Authors: Yihang Qiu, Zengrong Huang, Simin Tao, Hongda Zhang, Weiguo Li, Xinhua Lai, Rui Wang, Weiqiang Wang, Xingquan Li,
• Abstract summary: Current AI for EDA (AI-EDA) infrastructures remain fragmented, lacking comprehensive solutions for the entire data pipeline from design execution to AI integration.<n>This work introduces a unified open-source library for EDA (AiEDA) that addresses these issues.<n>AiEDA integrates multiple design-to-vector data representation techniques that transform diverse chip design data into universal multi-level vector representations.
• Score: 13.443294857996852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research has demonstrated that artificial intelligence (AI) can assist electronic design automation (EDA) in improving both the quality and efficiency of chip design. But current AI for EDA (AI-EDA) infrastructures remain fragmented, lacking comprehensive solutions for the entire data pipeline from design execution to AI integration. Key challenges include fragmented flow engines that generate raw data, heterogeneous file formats for data exchange, non-standardized data extraction methods, and poorly organized data storage. This work introduces a unified open-source library for EDA (AiEDA) that addresses these issues. AiEDA integrates multiple design-to-vector data representation techniques that transform diverse chip design data into universal multi-level vector representations, establishing an AI-aided design (AAD) paradigm optimized for AI-EDA workflows. AiEDA provides complete physical design flows with programmatic data extraction and standardized Python interfaces bridging EDA datasets and AI frameworks. Leveraging the AiEDA library, we generate iDATA, a 600GB dataset of structured data derived from 50 real chip designs (28nm), and validate its effectiveness through seven representative AAD tasks spanning prediction, generation, optimization and analysis. The code is publicly available at https://github.com/OSCC-Project/AiEDA, while the full iDATA dataset is being prepared for public release, providing a foundation for future AI-EDA research.

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