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

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, nonstandardized 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 multilevel 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 that bridge EDA datasets and AI frameworks. Leveraging the AiEDA library, we generate iDATA, a 600-GB dataset of structured data derived from 50 real chip designs (28 nm), and validate its effectiveness through five representative AAD tasks spanning prediction, generation, and optimization. The code of AiEDA is publicly available at https://github.com/OSCC-Project/AiEDA, providing a foundation for future AI-EDA research.