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

TL;DR
ForgeEDA is a large, diverse, open-source dataset for electronic design automation that supports benchmarking, analysis, and AI model training to advance IC design techniques.
Contribution
It introduces ForgeEDA, a comprehensive multimodal dataset that covers various circuit representations, enabling improved benchmarking and AI-driven advancements in EDA.
Findings
ForgeEDA effectively benchmarks state-of-the-art EDA algorithms.
The dataset exposes performance gaps in current methods.
ForgeEDA enhances AI model training for EDA tasks.
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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Taxonomy
TopicsFuzzy Logic and Control Systems · Educational Technology and Assessment · Power Systems and Technologies
