WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design

TL;DR

This paper introduces WACA-UNet, a novel neural network with a weakness-aware channel attention mechanism that significantly improves IR drop prediction accuracy in VLSI design by adaptively emphasizing important features.

Contribution

It proposes a Weakness-Aware Channel Attention mechanism integrated into a ConvNeXtV2-based U-Net for better feature representation in IR drop prediction.

Findings

Outperforms previous methods on ICCAD-2023 benchmark

Reduces mean absolute error by 61.1%

Improves F1-score by 71.0%

Abstract

Accurate spatial prediction of power integrity issues, such as IR drop, is critical for reliable VLSI design. However, traditional simulation-based solvers are computationally expensive and difficult to scale. We address this challenge by reformulating IR drop estimation as a pixel-wise regression task on heterogeneous multi-channel physical maps derived from circuit layouts. Prior learning-based methods treat all input layers (e.g., metal, via, and current maps) equally, ignoring their varying importance to prediction accuracy. To tackle this, we propose a novel Weakness-Aware Channel Attention (WACA) mechanism, which recursively enhances weak feature channels while suppressing over-dominant ones through a two-stage gating strategy. Integrated into a ConvNeXtV2-based attention U-Net, our approach enables adaptive and balanced feature representation. On the public ICCAD-2023 benchmark, our method outperforms the ICCAD-2023 contest winner by reducing mean absolute error by 61.1% and improving F1-score by 71.0%. These results demonstrate that channel-wise heterogeneity is a key inductive bias in physical layout analysis for VLSI.