{"title":"利用高斯混合模型估计泄漏分布","authors":"Hyun-jeong Kwon, Young Hwan Kim, Seokhyeong Kang","doi":"10.1109/ISOCC.2018.8649978","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel method which utilizes the Gaussian Mixture Model (GMM) to estimate the leakage distribution of a circuit. Our proposed method assumes that the leakage distribution can be represented using the GMM which can cover any continuous function. After the GMM clustering using the leakage simulation data, the leakage distribution of the input circuit can be obtained. The experimental results with the K-S test showed that the proposed method exhibited 1.82e+05 times larger p-value and 7.74e-01 times smaller K-S statistics compared to the state-of-the-art benchmark method on average.","PeriodicalId":127156,"journal":{"name":"2018 International SoC Design Conference (ISOCC)","volume":"13 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of Leakage Distribution Utilizing Gaussian Mixture Model\",\"authors\":\"Hyun-jeong Kwon, Young Hwan Kim, Seokhyeong Kang\",\"doi\":\"10.1109/ISOCC.2018.8649978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel method which utilizes the Gaussian Mixture Model (GMM) to estimate the leakage distribution of a circuit. Our proposed method assumes that the leakage distribution can be represented using the GMM which can cover any continuous function. After the GMM clustering using the leakage simulation data, the leakage distribution of the input circuit can be obtained. The experimental results with the K-S test showed that the proposed method exhibited 1.82e+05 times larger p-value and 7.74e-01 times smaller K-S statistics compared to the state-of-the-art benchmark method on average.\",\"PeriodicalId\":127156,\"journal\":{\"name\":\"2018 International SoC Design Conference (ISOCC)\",\"volume\":\"13 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International SoC Design Conference (ISOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISOCC.2018.8649978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International SoC Design Conference (ISOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISOCC.2018.8649978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of Leakage Distribution Utilizing Gaussian Mixture Model
In this paper, we propose a novel method which utilizes the Gaussian Mixture Model (GMM) to estimate the leakage distribution of a circuit. Our proposed method assumes that the leakage distribution can be represented using the GMM which can cover any continuous function. After the GMM clustering using the leakage simulation data, the leakage distribution of the input circuit can be obtained. The experimental results with the K-S test showed that the proposed method exhibited 1.82e+05 times larger p-value and 7.74e-01 times smaller K-S statistics compared to the state-of-the-art benchmark method on average.