Sketch-to-Style: Augmenting AI4EDA Dataset with Automatic Image Generative Framework

While many EDA tools now incorporate machine learning, and data-driven approaches from AI-assisted EDA (AI4EDA) prediction techniques shift the design process left, researchers require increasingly larger datasets to ensure model generalization. However, the scarcity of open-source IP designs, the insufficiency of open-source AI4EDA datasets, and lengthy tool runtimes contribute to the high time, economic, and effort costs of obtaining sufficient data. To address these challenges, we propose a Sketch-to-Style layout image generation framework <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, which rapidly produces high-quality yet meaningful layout images from limited data. To demonstrate its effectiveness, we focus on generating Design Rule Violation (DRV) feature maps, serving as an augmentation method for DRV prediction—an AI4EDA innovation. Our experiments show that integrating "fake" data from the proposed framework with real datasets steadily improves the performance of Structure Similarity Index Measure (SSIM), while improving 5.11% of the Area Under the Curve of the Receiver Operating Characteristic (AUC of ROC), 32.27% of accuracy and reducing 19.28% of False Positive Rate (FPR) in imbalanced category predictions at the same time, enhancing the model’s ability to learn DRV structural features and localization patterns. The proposed framework show great potential of generating image data for various prediction tasks.