{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:34:58Z","timestamp":1750221298369,"version":"3.41.0"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T00:00:00Z","timestamp":1532390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1550470"],"award-info":[{"award-number":["1550470"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2018,7,31]]},"abstract":"<jats:p>To protect multicores from soft-error perturbations, research has explored various resiliency schemes that provide high soft-error coverage. However, these schemes incur high performance and energy overheads. We observe that not all soft-error perturbations affect program correctness, and some soft-errors only affect program accuracy, i.e., the program completes with certain acceptable deviations from error free outcome. Thus, it is practical to improve processor efficiency by trading off resiliency overheads with program accuracy. This article proposes the idea of declarative resilience that selectively applies strong resiliency schemes for code regions that are crucial for program correctness (crucial code) and lightweight resiliency for code regions that are susceptible to program accuracy deviations as a result of soft-errors (non-crucial code). At the application level, crucial and non-crucial code is identified based on its impact on the program outcome. A cross-layer architecture enables efficient resilience along with holistic soft-error coverage. Only program accuracy is compromised in the worst-case scenario of a soft-error strike during non-crucial code execution. For a set of machine-learning and graph analytic benchmarks, declarative resilience reduces performance overhead over a state-of-the-art system that applies strong resiliency for all program code regions from \u223c 1.43\u00d7 to \u223c 1.2\u00d7.<\/jats:p>","DOI":"10.1145\/3210559","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T14:41:49Z","timestamp":1532443309000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Declarative Resilience"],"prefix":"10.1145","volume":"17","author":[{"given":"Hamza","family":"Omar","sequence":"first","affiliation":[{"name":"University of Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingchuan","family":"Shi","sequence":"additional","affiliation":[{"name":"University of Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masab","family":"Ahmad","sequence":"additional","affiliation":[{"name":"University of Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Halit","family":"Dogan","sequence":"additional","affiliation":[{"name":"University of Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omer","family":"Khan","sequence":"additional","affiliation":[{"name":"University of Connecticut, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2015.11"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2155620.2155627"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/320080.320111"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2005.70"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1454115.1454128"},{"key":"e_1_2_1_6_1","unstructured":"Mariusz Bojarski Davide Del Testa Daniel Dworakowski Bernhard Firner Beat Flepp Prasoon Goyal Lawrence D. Jackel Mathew Monfort Urs Muller Jiakai Zhang Xin Zhang Jake Zhao and Karol Zieba. 2016. End to end learning for self-driving cars. CoRR abs\/1604.07316 (2016). Retrieved from http:\/\/arxiv.org\/abs\/1604.07316.  Mariusz Bojarski Davide Del Testa Daniel Dworakowski Bernhard Firner Beat Flepp Prasoon Goyal Lawrence D. Jackel Mathew Monfort Urs Muller Jiakai Zhang Xin Zhang Jake Zhao and Karol Zieba. 2016. End to end learning for self-driving cars. CoRR abs\/1604.07316 (2016). Retrieved from http:\/\/arxiv.org\/abs\/1604.07316."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2509136.2509546"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2013.2266917"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-4371(02)01545-5"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1815961.1816026"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2150976.2151008"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2012.48"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1735970.1736063"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2011.2159586"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2011.6081430"},{"volume-title":"Proceedings of the IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN\u201912)","author":"Hari S. K. S.","key":"e_1_2_1_17_1"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.585157"},{"key":"e_1_2_1_19_1","unstructured":"Texas Instruments. 2016. Texas instruments soft error FAQs. Retrieved from http:\/\/www.ti.com\/support-quality\/faqs\/soft-error-rate-faqs.html.  Texas Instruments. 2016. Texas instruments soft error FAQs. Retrieved from http:\/\/www.ti.com\/support-quality\/faqs\/soft-error-rate-faqs.html."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2002.806806"},{"volume-title":"Proceedings of the Symposium on VLSI Circuits. Digest of Technical Papers (IEEE Cat. No. 01CH37185)","author":"Karnik T.","key":"e_1_2_1_21_1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2014.33"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2502524.2502530"},{"volume-title":"Proceedings of the 9th IEEE International Conference on Design Technology of Integrated Systems in Nanoscale Era (DTIS\u201914)","author":"Kooli M.","key":"e_1_2_1_24_1"},{"key":"e_1_2_1_25_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 F. Pereira C. J. C. Burges L. Bottou and K. Q. Weinberger (Eds.). Curran Associates Inc. 1097--1105. Retrieved from http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.   Alex Krizhevsky Ilya Sutskever and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 F. Pereira C. J. C. Burges L. Bottou and K. Q. Weinberger (Eds.). Curran Associates Inc. 1097--1105. Retrieved from http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_27_1","unstructured":"Jure Leskovec Kevin J. Lang Anirban Dasgupta and Michael W. Mahoney. 2008. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. CoRR abs\/0810.1355 (2008). Retrieved from http:\/\/arxiv.org\/abs\/0810.1355.  Jure Leskovec Kevin J. Lang Anirban Dasgupta and Michael W. Mahoney. 2008. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. CoRR abs\/0810.1355 (2008). Retrieved from http:\/\/arxiv.org\/abs\/0810.1355."},{"volume-title":"Proceedings of the IEEE International Conference on Robotics and Automation (ICRA\u201912)","author":"Li H.","key":"e_1_2_1_28_1"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1669112.1669172"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488809"},{"volume":"17","volume-title":"Nature Neuroscience","author":"Lichtman J. W.","key":"e_1_2_1_31_1"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961296.1950391"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2015.13"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2008.3"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2830772.2830790"},{"volume-title":"Proceedings of the 16th International Symposium on High-Performance Computer Architecture (HPCS\u201910)","author":"Miller J. E.","key":"e_1_2_1_36_1"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1806799.1806808"},{"volume-title":"Proceedings of the 29th Annual International Symposium on Computer Architecture. 99--110","author":"Mukherjee S. S.","key":"e_1_2_1_38_1"},{"volume-title":"Proceedings of the IEEE International Conference on Robotics and Automation (ICRA\u201911)","author":"Murphy L.","key":"e_1_2_1_39_1"},{"key":"e_1_2_1_40_1","unstructured":"Michael A. Nielsen. 2015. Neural Networks and Deep Learning. Determination Press.  Michael A. Nielsen. 2015. Neural Networks and Deep Learning. Determination Press."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/12.980007"},{"volume-title":"Proceedings of the IEEE International Conference on Computer Design (ICCD\u201917)","author":"Omar H.","key":"e_1_2_1_42_1"},{"volume-title":"Proceedings of the IEEE 14th International Symposium on High Performance Computer Architecture. 393--404","author":"Rashid M. W.","key":"e_1_2_1_43_1"},{"volume-title":"Proceedings of the IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN\u201908)","author":"Reddy V.","key":"e_1_2_1_44_1"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2593069.2593127"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2005.34"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1113841.1113843"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10836-013-5416-6"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2198665"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.5555\/795672.796966"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2540708.2540711"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/1669112.1669129"},{"volume-title":"Proceedings of the GPU Technology Conference.","author":"Sato I.","key":"e_1_2_1_53_1"},{"volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201911)","author":"Sermanet P.","key":"e_1_2_1_54_1"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488755"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2014.2365204"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2013.262"},{"key":"e_1_2_1_58_1","unstructured":"Qingchuan Shi Hamza Omar and Omer Khan. 2017. Exploiting the tradeoff between program accuracy and soft-error resiliency overhead for machine learning workloads. CoRR abs\/1707.02589 (2017). Retrieved from http:\/\/arxiv.org\/abs\/1707.02589.  Qingchuan Shi Hamza Omar and Omer Khan. 2017. Exploiting the tradeoff between program accuracy and soft-error resiliency overhead for machine learning workloads. CoRR abs\/1707.02589 (2017). Retrieved from http:\/\/arxiv.org\/abs\/1707.02589."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.5555\/647883.738394"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025133"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1147\/rd.483.0295"},{"key":"e_1_2_1_62_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014). Retrieved from http:\/\/arxiv.org\/abs\/1409.1556.  Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014). Retrieved from http:\/\/arxiv.org\/abs\/1409.1556."},{"volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201911)","author":"Stallkamp J.","key":"e_1_2_1_63_1"},{"volume-title":"Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI\u201995)","year":"1995","author":"Stentz Anthony","key":"e_1_2_1_64_1"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/NOCS.2012.31"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2015.7357189"},{"volume-title":"Proceedings of the 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO\u201916)","author":"Venkatagiri R.","key":"e_1_2_1_67_1"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2015.7357187"},{"volume-title":"Proceedings of the ACM\/IEEE 41st International Symposium on Computer Architecture (ISCA\u201914)","author":"Wadden J.","key":"e_1_2_1_69_1"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2005.82"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3210559","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3210559","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3210559","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:14Z","timestamp":1750212794000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3210559"}},"subtitle":["A Holistic Soft-Error Resilient Multicore Architecture that Trades off Program Accuracy for Efficiency"],"short-title":[],"issued":{"date-parts":[[2018,7,24]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,7,31]]}},"alternative-id":["10.1145\/3210559"],"URL":"https:\/\/doi.org\/10.1145\/3210559","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"type":"print","value":"1539-9087"},{"type":"electronic","value":"1558-3465"}],"subject":[],"published":{"date-parts":[[2018,7,24]]},"assertion":[{"value":"2017-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-07-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}