{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:32:44Z","timestamp":1750221164067,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,7,17]],"date-time":"2018-07-17T00:00:00Z","timestamp":1531785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,7,17]]},"DOI":"10.1145\/3232195.3232208","type":"proceedings-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T13:33:51Z","timestamp":1545399231000},"page":"25-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["An Aging Resilient Neural Network Architecture"],"prefix":"10.1145","author":[{"given":"Seyed Nima","family":"Mozaffari","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL"}]},{"given":"Krishna Prasad","family":"Gnawali","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL"}]},{"given":"Spyros","family":"Tragoudas","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL"}]}],"member":"320","published-online":{"date-parts":[[2018,7,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2014.2359717"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/23\/7\/075201"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/el.2012.2295"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727219"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2016.2597152"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2016.2528739"},{"volume-title":"2017 International Joint Conference on Neural Networks (IJCNN). 3527--3534","author":"Hasan R.","key":"e_1_3_2_1_7_1","unstructured":"R. Hasan , T. M. Taha , and C. Yakopcic . 2017. On-chip training of memristor based deep neural networks . In 2017 International Joint Conference on Neural Networks (IJCNN). 3527--3534 . R. Hasan, T. M. Taha, and C. Yakopcic. 2017. On-chip training of memristor based deep neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN). 3527--3534."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2010.2040125"},{"volume-title":"2012 IEEE 15th International Symposium on Design and Diagnostics of Electronic Circuits Systems (DDECS). 348--353","author":"Khan S.","key":"e_1_3_2_1_9_1","unstructured":"S. Khan , S. Hamdioui , H. Kukner , P. Raghavan , and F. Catthoor . 2012. BTI impact on logical gates in nano-scale CMOS technology . In 2012 IEEE 15th International Symposium on Design and Diagnostics of Electronic Circuits Systems (DDECS). 348--353 . S. Khan, S. Hamdioui, H. Kukner, P. Raghavan, and F. Catthoor. 2012. BTI impact on logical gates in nano-scale CMOS technology. In 2012 IEEE 15th International Symposium on Design and Diagnostics of Electronic Circuits Systems (DDECS). 348--353."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2011.2166749"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897937.2898011"},{"volume-title":"1999 Symposium on VLSI Technology. Digest of Technical Papers (IEEE Cat. No.99CH36325)","author":"Kimizuka N.","key":"e_1_3_2_1_12_1","unstructured":"N. Kimizuka , T. Yamamoto , T. Mogami , K. Yamaguchi , K. Imai , and T. Horiuchi . 1999. The impact of bias temperature instability for direct-tunneling ultra-thin gate oxide on MOSFET scaling . In 1999 Symposium on VLSI Technology. Digest of Technical Papers (IEEE Cat. No.99CH36325) . 73--74. N. Kimizuka, T. Yamamoto, T. Mogami, K. Yamaguchi, K. Imai, and T. Horiuchi. 1999. The impact of bias temperature instability for direct-tunneling ultra-thin gate oxide on MOSFET scaling. In 1999 Symposium on VLSI Technology. Digest of Technical Papers (IEEE Cat. No.99CH36325). 73--74."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2015.2433536"},{"volume-title":"2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel. 1--5.","author":"Kvatinsky S.","key":"e_1_3_2_1_14_1","unstructured":"S. Kvatinsky , K. Talisveyberg , D. Fliter , A. Kolodny , U. C. Weiser , and E. G. Friedman . 2012. Models of memristors for SPICE simulations . In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel. 1--5. S. Kvatinsky, K. Talisveyberg, D. Fliter, A. Kolodny, U. C. Weiser, and E. G. Friedman. 2012. Models of memristors for SPICE simulations. In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel. 1--5."},{"key":"e_1_3_2_1_15_1","volume-title":"d.}. The MNIST Database of Handwritten Digits. ({n. d.}). Retrieved","author":"LeCun Y.","year":"2018","unstructured":"Y. LeCun , C. Cortes , and C.J. C. Burges . {n. d.}. The MNIST Database of Handwritten Digits. ({n. d.}). Retrieved March 26 2018 from http:\/\/yann.lecun.com\/exdb\/mnist\/ Y. LeCun, C. Cortes, and C.J. C. Burges. {n. d.}. The MNIST Database of Handwritten Digits. ({n. d.}). Retrieved March 26 2018 from http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"e_1_3_2_1_16_1","volume-title":"Automation Test in Europe Conference Exhibition (DATE)","author":"Li Z.","year":"2017","unstructured":"Z. Li , A. Ren , J. Li , Q. Qiu , B. Yuan , J. Draper , and Y. Wang . 2017. Structural design optimization for deep convolutional neural networks using stochastic computing. In Design , Automation Test in Europe Conference Exhibition (DATE) , 2017 . 250--253. Z.Li, A. Ren, J.Li, Q. Qiu, B. Yuan, J. Draper, and Y. Wang. 2017. Structural design optimization for deep convolutional neural networks using stochastic computing. In Design, Automation Test in Europe Conference Exhibition (DATE), 2017. 250--253."},{"volume-title":"18th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). 1--7.","author":"Mahapatra S.","key":"e_1_3_2_1_17_1","unstructured":"S. Mahapatra , A. E. Islam , S. Deora , V. D. Maheta , K. Joshi , and M. A. Alam . 2011. Characterization and modeling of NBTI stress, recovery, material dependence and AC degradation using R-D framework . In 18th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). 1--7. S. Mahapatra, A. E. Islam, S. Deora, V. D. Maheta, K. Joshi, and M. A. Alam. 2011. Characterization and modeling of NBTI stress, recovery, material dependence and AC degradation using R-D framework. In 18th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). 1--7."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2093145.2093151"},{"volume-title":"2nd Asia Symposium on Quality Electronic Design (ASQED). 356--360","author":"Mozaffari S. N.","key":"e_1_3_2_1_19_1","unstructured":"S. N. Mozaffari and A. Afzali-Kusha . 2010. Statistical model for subthreshold current considering process variations . In 2nd Asia Symposium on Quality Electronic Design (ASQED). 356--360 . S. N. Mozaffari and A. Afzali-Kusha. 2010. Statistical model for subthreshold current considering process variations. In 2nd Asia Symposium on Quality Electronic Design (ASQED). 356--360."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2016.2608863"},{"volume-title":"2015 International Joint Conference on Neural Networks (IJCNN). 1--7.","author":"Nair M. V.","key":"e_1_3_2_1_21_1","unstructured":"M. V. Nair and P. Dudek . 2015. Gradient-descent-based learning in memristive crossbar arrays . In 2015 International Joint Conference on Neural Networks (IJCNN). 1--7. M. V. Nair and P. Dudek. 2015. Gradient-descent-based learning in memristive crossbar arrays. In 2015 International Joint Conference on Neural Networks (IJCNN). 1--7."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"M. Prezois F. Merrikh-Bayat B.D. Hoskins G.C. Adam K.K. Likharev and D.B. Strukov. 2016. Training and Operation of an Integrated neuromorphic network based on Metal-Oxide Memristors. nature Letter 521 1 (2016) 61--64.  M. Prezois F. Merrikh-Bayat B.D. Hoskins G.C. Adam K.K. Likharev and D.B. Strukov. 2016. Training and Operation of an Integrated neuromorphic network based on Metal-Oxide Memristors. nature Letter 521 1 (2016) 61--64.","DOI":"10.1038\/nature14441"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/DFT.2009.20"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNANO.2013.2286424"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1088\/0268-1242\/27\/6\/065010"},{"key":"e_1_3_2_1_26_1","volume-title":"High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Applied Physics Letter 98","author":"Yu S.","year":"2011","unstructured":"S. Yu , Y. Wu , and H.-S. Philip Wong . 2011. High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Applied Physics Letter 98 ( 2011 ). S. Yu, Y. Wu, and H.-S. Philip Wong. 2011. High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Applied Physics Letter 98 (2011)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628081"}],"event":{"name":"NANOARCH '18: IEEE\/ACM International Symposium on Nanoscale Architectures","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CS"],"location":"Athens Greece","acronym":"NANOARCH '18"},"container-title":["Proceedings of the 14th IEEE\/ACM International Symposium on Nanoscale Architectures"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3232195.3232208","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3232195.3232208","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:08:46Z","timestamp":1750208926000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3232195.3232208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,17]]},"references-count":27,"alternative-id":["10.1145\/3232195.3232208","10.1145\/3232195"],"URL":"https:\/\/doi.org\/10.1145\/3232195.3232208","relation":{},"subject":[],"published":{"date-parts":[[2018,7,17]]},"assertion":[{"value":"2018-07-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}