Deep Learning-Based Early-Stage IR-Drop Estimation via CNN Surrogate Modeling

TL;DR

This paper introduces a CNN-based surrogate model for rapid early-stage IR-drop estimation in VLSI design, enabling fast and accurate IR-drop heatmap predictions to assist early design decisions.

Contribution

It presents a novel U-Net-based deep learning framework trained on synthetic data for efficient IR-drop prediction, reducing reliance on costly physics-based signoff tools during early design phases.

Findings

Accurately predicts IR-drop heatmaps with millisecond inference time.

Outperforms traditional methods in speed while maintaining high accuracy.

Provides a practical tool for early-stage power integrity analysis.

Abstract

IR-drop is a critical power integrity challenge in modern VLSI designs that can cause timing degradation, reliability issues, and functional failures if not detected early in the design flow. Conventional IR-drop analysis relies on physics-based signoff tools, which provide high accuracy but incur significant computational cost and require near-final layout information, making them unsuitable for rapid early-stage design exploration. In this work, we propose a deep learning-based surrogate modeling approach for early-stage IR-drop estimation using a CNN. The task is formulated as a dense pixel-wise regression problem, where spatial physical layout features are mapped directly to IR-drop heatmaps. A U-Net-based encoder-decoder architecture with skip connections is employed to effectively capture both local and global spatial dependencies within the layout. The model is trained on a physics-inspired synthetic dataset generated by us, which incorporates key physical factors including power grid structure, cell density distribution, and switching activity. Model performance is evaluated using standard regression metrics such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Experimental results demonstrate that the proposed approach can accurately predict IR-drop distributions with millisecond-level inference time, enabling fast pre-signoff screening and iterative design optimization. The proposed framework is intended as a complementary early-stage analysis tool, providing designers with rapid IR-drop insight prior to expensive signoff analysis. The implementation, dataset generation scripts, and the interactive inference application are publicly available at: https://github.com/riteshbhadana/IR-Drop-Predictor. The live application can be accessed at: https://ir-drop-predictor.streamlit.app/.