ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

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.