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PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing

Published: 12 June 2024 Publication History

Abstract

As feature sizes shrink, the on-chip power grid (PG) faces serious power integrity issues, and static IR drop analysis becomes critical for PG design and optimization. Many machine learning (ML) based methods have been proposed to address the inefficiencies of traditional numerical methods. However, many previous works have ignored the problems of feature confusion and imbalance IR drop distribution. In this work, we propose novel feature augmentation and selection methods to solve the feature confusion problem and use the label distribution smoothing (LDS) technique to handle unbalanced labels. Importantly, we design a static IR drop analysis model for PG using the Attention U-Net architecture (PGAU). Furthermore, two real-world datasets are used for evaluation. Experiments show that our model outperforms baselines, with a 2.6% improvement in the correlation coefficient (CC) and a 22.2% reduction in the mean absolute error (MAE). Moreover, our model is highly transferable and performs better against never-before-seen designs.

References

[1]
[1] Laung-Terng Wang, Yao-Wen Chang, and Kwang-Ting Tim Cheng. Electronic Design Automation: Synthesis, Verification, and Test. Morgan Kaufmann, 2009.
[2]
[2] Haoxing Ren and Jiang Hu. Machine Learning Applications in Electronic Design Automation. Springer, 2022.
[3]
[3] Chi-Hsien Pao, An-Yu Su, and Yu-Min Lee. XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network. IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), pp. 1307–1310, 2020.
[4]
[4] Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 785–794, 2016.
[5]
[5] Zhiyao Xie, Haoxing Ren, Brucek Khailany, Ye Sheng, Santosh Santosh, Jiang Hu, and Yiran Chen. PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network. IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), pp. 13–18, 2020.
[6]
[6] Vidya A Chhabria, Vipul Ahuja, Ashwath Prabhu, Nikhil Patil, Palkesh Jain, and Sachin S Sapatnekar. Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), pp. 690–696, 2021.
[7]
[7] Vidya A Chhabria, Yanqing Zhang, Haoxing Ren, Ben Keller, Brucek Khailany, and Sachin S Sapatnekar. MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification. IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), pp. 1825–1828, 2021.
[8]
[8] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, 2015.
[9]
[9] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241, 2015.
[10]
[10] Nahian Siddique, Sidike Paheding, Colin P Elkin, and Vijay Devabhaktuni. U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications. IEEE Access, vol. 9, pp. 82031–82057, 2021.
[11]
[11] Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, vol. 106, pp. 249–259, 2018.
[12]
[12] Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, and Dina Katabi. Delving into Deep Imbalanced Regression. International Conference on Machine Learning (ICML), pp. 11842–11851, 2021.
[13]
[13] Jialv Zou, Xinggang Wang, Jiahao Guo, Wenyu Liu, Qian Zhang, and Chang Huang. Circuit as Set of Points. Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
[14]
[14] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA), pp. 3–11, 2018.
[15]
[15] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, et al. Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6881–6890, 2021.
[16]
[16] Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, et al. Searching for MobileNetV3. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1314–1324, 2019.
[17]
[17] Zhuomin Chai, Yuxiang Zhao, Wei Liu, Yibo Lin, Runsheng Wang, and Ru Huang. CircuitNet: An Open-Source Dataset for Machine Learning in VLSI CAD Applications With Improved Domain-Specific Evaluation Metric and Learning Strategies. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 42, no. 12, pp. 5034–5047, 2023.
[18]
[18] Vidya A Chhabria, Kishor Kunal, Masoud Zabihi, and Sachin S Sapatnekar. BeGAN: Power Grid Benchmark Generation Using a Process-portable GAN-based Methodology. IEEE/ACM International Conference On Computer Aided Design (ICCAD), pp. 1–8, 2021.

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  1. PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing

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    cover image ACM Conferences
    GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024
    June 2024
    797 pages
    ISBN:9798400706059
    DOI:10.1145/3649476
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 12 June 2024

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    Author Tags

    1. Attention
    2. IR drop
    3. Power grid
    4. U-Net

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    GLSVLSI '24: Great Lakes Symposium on VLSI 2024
    June 12 - 14, 2024
    FL, Clearwater, USA

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    Overall Acceptance Rate 312 of 1,156 submissions, 27%

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