ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

Zhengyuan Shi; Zeju Li; Chengyu Ma; Yunhao Zhou; Ziyang Zheng; Jiawei Liu; Hongyang Pan; Lingfeng Zhou; Kezhi Li; Jiaying Zhu; Lingwei Yan; Zhiqiang He; Chenhao Xue; Wentao Jiang; Fan Yang; Guangyu Sun; Xiaoyan Yang; Gang Chen; Chuan Shi; Zhufei Chu; Jun Yang; Qiang Xu

arXiv · 2505.02016

ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

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Abstract

We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RTL) code, Post-mapping (PM) netlists, And-Inverter Graphs (AIGs), and placed netlists, enabling comprehensive analysis and development. We demonstrate ForgeEDA's utility by benchmarking state-of-the-art EDA algorithms on critical tasks such as Power, Performance, and Area (PPA) optimization, highlighting its ability to expose performance gaps and drive advancements. Additionally, ForgeEDA's scale and diversity facilitate the training of AI models for EDA tasks, demonstrating its potential to improve model performance and generalization. By addressing limitations in existing datasets, ForgeEDA aims to catalyze breakthroughs in modern IC design and support the next generation of innovations in EDA.

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Zhengyuan Shi, Zeju Li, Chengyu Ma, Yunhao Zhou, Ziyang Zheng, Jiawei Liu, Hongyang Pan, Lingfeng Zhou, Kezhi Li, Jiaying Zhu, Lingwei Yan, Zhiqiang He, Chenhao Xue, Wentao Jiang, Fan Yang, Guangyu Sun, Xiaoyan Yang, Gang Chen, Chuan Shi, Zhufei Chu, Jun Yang, Qiang Xu. 2025-05-04. ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA. https://arxiv.org/abs/2505.02016

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