Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies
IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/13/10/1807 |
| _version_ | 1797513148995993600 |
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| author | Pengcheng Huang Chiyuan Ma Zhenyu Wu |
| author_facet | Pengcheng Huang Chiyuan Ma Zhenyu Wu |
| author_sort | Pengcheng Huang |
| collection | DOAJ |
| description | IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk. |
| first_indexed | 2024-03-10T06:10:39Z |
| format | Article |
| id | doaj.art-a12dcdd4415f4d3db03342da8e0d699f |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T06:10:39Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-a12dcdd4415f4d3db03342da8e0d699f2023-11-22T20:09:24ZengMDPI AGSymmetry2073-89942021-09-011310180710.3390/sym13101807Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET TechnologiesPengcheng Huang0Chiyuan Ma1Zhenyu Wu2Microelectronics and Microprocessor Institute, School of Computer, National University of Defense Technology, Changsha 410073, ChinaMicroelectronics and Microprocessor Institute, School of Computer, National University of Defense Technology, Changsha 410073, ChinaMicroelectronics and Microprocessor Institute, School of Computer, National University of Defense Technology, Changsha 410073, ChinaIR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk.https://www.mdpi.com/2073-8994/13/10/1807IR-drop (IRD)machine learningengineer change order (ECO)XGBoost |
| spellingShingle | Pengcheng Huang Chiyuan Ma Zhenyu Wu Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies Symmetry IR-drop (IRD) machine learning engineer change order (ECO) XGBoost |
| title | Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies |
| title_full | Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies |
| title_fullStr | Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies |
| title_full_unstemmed | Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies |
| title_short | Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies |
| title_sort | fast dynamic ir drop prediction using machine learning in bulk finfet technologies |
| topic | IR-drop (IRD) machine learning engineer change order (ECO) XGBoost |
| url | https://www.mdpi.com/2073-8994/13/10/1807 |
| work_keys_str_mv | AT pengchenghuang fastdynamicirdroppredictionusingmachinelearninginbulkfinfettechnologies AT chiyuanma fastdynamicirdroppredictionusingmachinelearninginbulkfinfettechnologies AT zhenyuwu fastdynamicirdroppredictionusingmachinelearninginbulkfinfettechnologies |