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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.19197 |
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| _version_ | 1866912501107523584 |
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| author | Seo, Youngmin Kwon, Yunhyeong Park, Younghun Kim, HwiRyong Eum, Seungho Kim, Jinha Song, Taigon Kim, Juho Park, Unsang |
| author_facet | Seo, Youngmin Kwon, Yunhyeong Park, Younghun Kim, HwiRyong Eum, Seungho Kim, Jinha Song, Taigon Kim, Juho Park, Unsang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design Seo, Youngmin Kwon, Yunhyeong Park, Younghun Kim, HwiRyong Eum, Seungho Kim, Jinha Song, Taigon Kim, Juho Park, Unsang Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition B.7.2; I.5.1; I.2.10; I.5.4 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. |
| title | WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Design |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition B.7.2; I.5.1; I.2.10; I.5.4 |
| url | https://arxiv.org/abs/2507.19197 |