ICDaIR: Distribution-aware Static IR Drop Prediction Flow Based on Image Classification
Article No.: 4, Pages 1 - 6
Abstract
During the integrated circuit design process, the maximum IR drop value is often given more attention. The frequency of the maximum IR drop in the actual circuits presents an uneven dispersion, i.e., long-tail distribution. To address this problem, this paper introduces ICDaIR, a distribution-aware static IR drop prediction flow based on image classification. ICDaIR first utilizes a random forest classifier to categorize sub-regions, obtained from segmentation, into three groups. There are small differences in sub-regional samples within each category. Then, a U-Net-based prediction model is used to obtain the IR drop map for each class of sub-region. The overall IR drop map is derived by combining the values from these sub-regions. On the dataset with a maximum layout area of 0.69μm2, compared with SOTA and the model without classification, the MAE of our model decreases by 50.5% and 14.4% respectively, with the prediction error controlled within 0.2029mV.
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Index Terms
- ICDaIR: Distribution-aware Static IR Drop Prediction Flow Based on Image Classification
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Published In
September 2024
321 pages
ISBN:9798400706998
DOI:10.1145/3670474
- General Chairs:
- Hussam Amrouch,
- Jiang Hu,
- Program Chairs:
- Siddharth Garg,
- Yibo Lin
Copyright � 2024 ACM.
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Publication History
Published: 09 September 2024
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- Research-article
- Research
- Refereed limited
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- the Fundamental Research Funds for the Central Universities
- the National Natural Science Foundation of China
- the Natural Science Foundation of Jiangsu Province
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MLCAD '24
Sponsor:
MLCAD '24: 2024 ACM/IEEE International Symposium on Machine Learning for CAD
September 9 - 11, 2024
UT, Salt Lake City, USA
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MLCAD '24 Paper Acceptance Rate 35 of 83 submissions, 42%;
Overall Acceptance Rate 35 of 83 submissions, 42%
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