IR drop Prediction Based on Machine Learning and Pattern Reduction-Reference-Cited by-同舟云学术

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IR drop Prediction Based on Machine Learning and Pattern Reduction

Published:2024-06-12 Issue: Volume: Page:516-519
ISSN:
Container-title:Proceedings of the Great Lakes Symposium on VLSI 2024
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Author:

Chang Yong-Fong¹^ORCID,Chen Yung-Chih²^ORCID,Cheng Yu-Chen¹^ORCID,Lin Shu-Hong²^ORCID,Lin Che-Hsu²^ORCID,Chen Chun-Yuan¹^ORCID,Chen Yu-Hsuan¹^ORCID,Lee Yu-Che¹^ORCID,Lin Jia-Wei³^ORCID,Pao Hsun-Wei³^ORCID,Chang Shih-Chieh¹^ORCID,Li Yi-Ting⁴^ORCID,Wang Chun-Yao¹^ORCID

Affiliation:

1. National Tsing Hua University, Taiwan

2. National Taiwan University of Science and Technology, Taiwan

3. MediaTek Inc., Taiwan

4. Computer Science, National Tsing Hua University, Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3649476.3658775

Reference17 articles.

1. Ansys 2018. RedHawk User Manual. Ansys.

2. Cadence [n. d.]. Voltus User Manual. Cadence.

3. Chi-Hsien Pao et al. 2020. XGBIR: An XGBoost-based IR drop predictor for power delivery network. In Design, Automation & Test in Europe Conference & Exhibition. 1307–1310.

4. Chia-Tung Ho et al. 2019. IncPIRD: Fast learning-based prediction of incremental IR drop. In International Conference on Computer-Aided Design. 1–8.

5. Howard H. Chen et al. 1997. Power supply noise analysis methodology for deep-submicron VLSI chip design. In Design Automation Conference. 638–643.

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