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Embed. Comput. Syst."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>\n            Silent Data Corruption (SDC) is a serious reliability issue in many domains, including embedded systems. However, current protection techniques are brittle and do not allow programmers to trade off performance for SDC coverage. Further, many require tens of thousands of fault-injection experiments, which are highly time- and resource-intensive. In this article, we propose two empirical models,\n            <jats:italic>SDCTune<\/jats:italic>\n            and\n            <jats:italic>SDCAuto<\/jats:italic>\n            , to predict the SDC proneness of a program\u2019s data. Both models are based on static and dynamic features of the program alone and do not require fault injections to be performed. The main difference between them is that\n            <jats:italic>SDCTune<\/jats:italic>\n            requires manual tuning while\n            <jats:italic>SDCAuto<\/jats:italic>\n            is completely automated, using machine-learning algorithms.\n          <\/jats:p>\n          <jats:p>\n            We then develop an algorithm using both models to selectively protect the most SDC-prone data in the program subject to a given performance overhead bound. Our results show that both models are accurate at predicting the relative SDC rate of an application compared to fault injection, for a fraction of the time taken. Further, in terms of efficiency of detection (i.e., ratio of SDC coverage provided to performance overhead), our technique outperforms full duplication by a factor of 0.78x to 1.65x with the\n            <jats:italic>SDCTune<\/jats:italic>\n            model and 0.62x to 0.96x with\n            <jats:italic>SDCAuto<\/jats:italic>\n            model.\n          <\/jats:p>","DOI":"10.1145\/3014586","type":"journal-article","created":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T17:42:39Z","timestamp":1490722959000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Configurable Detection of SDC-causing Errors in Programs"],"prefix":"10.1145","volume":"16","author":[{"given":"Qining","family":"Lu","sequence":"first","affiliation":[{"name":"University of British Columbia, Vancouver BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanpeng","family":"Li","sequence":"additional","affiliation":[{"name":"University of British Columbia, Vancouver BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karthik","family":"Pattabiraman","sequence":"additional","affiliation":[{"name":"University of British Columbia, Vancouver BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meeta S.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Founder Shumee Toys, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jude A.","family":"Rivers","sequence":"additional","affiliation":[{"name":"Technology Consultant, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,3,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/125826.125925"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1454115.1454128"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2005.110"},{"key":"e_1_2_1_4_1","unstructured":"L. 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Master\u2019s Thesis, Department of Electrical Engineering, Princeton University, Princeton, NJ."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC.2005.19"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/EDCC.2006.9"},{"key":"e_1_2_1_22_1","volume-title":"5: Programs for Machine Learning","author":"Quinlan John Ross","unstructured":"John Ross Quinlan . 1993. C4. 5: Programs for Machine Learning . Vol. 1 . Morgan Kaufmann , San Francisco, CA . John Ross Quinlan. 1993. C4. 5: Programs for Machine Learning. Vol. 1. Morgan Kaufmann, San Francisco, CA."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2005.34"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2008.4630072"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488755"},{"key":"e_1_2_1_26_1","unstructured":"Premkishore Shivakumar Michael Kistler Stephen W. Keckler Doug Burger and Lorenzo Alvisi. 2002. Modeling the effect of technology trends on the soft error rate of combinational logic (DSN\u201902). 389--398.   Premkishore Shivakumar Michael Kistler Stephen W. Keckler Doug Burger and Lorenzo Alvisi. 2002. Modeling the effect of technology trends on the soft error rate of combinational logic (DSN\u201902). 389--398."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.119549"},{"key":"e_1_2_1_28_1","volume-title":"Geng Daniel Liu, and W.-M. Hwu","author":"Stratton John A.","year":"2012","unstructured":"John A. Stratton , Christopher Rodrigues , I.- Jui Sung , Nady Obeid , Li-Wen Chang , Nasser Anssari , Geng Daniel Liu, and W.-M. Hwu . 2012 . Parboil : A revised benchmark suite for scientific and commercial throughput computing. Center for Reliable and High-Performance Computing . John A. Stratton, Christopher Rodrigues, I.-Jui Sung, Nady Obeid, Li-Wen Chang, Nasser Anssari, Geng Daniel Liu, and W.-M. Hwu. 2012. 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