{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:40:55Z","timestamp":1758127255758,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"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":[[2022,10,30]]},"DOI":"10.1145\/3508352.3549400","type":"proceedings-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T12:10:54Z","timestamp":1671711054000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DynaPAT"],"prefix":"10.1145","author":[{"given":"Thai-Hoang","family":"Nguyen","sequence":"first","affiliation":[{"name":"Sungkyunkwan University, Suwon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"National University of Sciences and Technology, Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon-Sung","family":"Yang","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASSCC.2014.7008879"},{"volume-title":"2018 Non-Volatile Memory Technology Symposium (NVMTS). IEEE, 1--4.","author":"Boybat I.","key":"e_1_3_2_1_2_1","unstructured":"I. Boybat, S. R. Nandakumar, M. Le Gallo, B. Rajendran, Y. Leblebici, A. Sebastian, and E. Eleftheriou. 2018. Impact of conductance drift on multi-PCM synaptic architectures. In 2018 Non-Volatile Memory Technology Symposium (NVMTS). IEEE, 1--4."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7926952"},{"key":"e_1_3_2_1_4_1","volume-title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes. arXiv preprint arXiv:2106.04803","author":"Dai Zihang","year":"2021","unstructured":"Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan. 2021. CoAtNet: Marrying Convolution and Attention for All Data Sizes. arXiv preprint arXiv:2106.04803 (2021)."},{"key":"e_1_3_2_1_5_1","first-page":"21","article-title":"An overview of phase-change memory device physics","volume":"53","author":"Gallo Manuel Le","year":"2020","unstructured":"Manuel Le Gallo and Abu Sebastian. 2020. An overview of phase-change memory device physics. IOP Publishing 53, 21 (mar 2020), 213002.","journal-title":"IOP Publishing"},{"key":"e_1_3_2_1_6_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han Song","year":"2015","unstructured":"Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_1_7_1","volume-title":"Robust Machine Learning Systems: Reliability and Security for Deep Neural Networks. In 2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS). 257--260","author":"Hanif Muhammad Abdullah","year":"2018","unstructured":"Muhammad Abdullah Hanif, Faiq Khalid, Rachmad Vidya Wicaksana Putra, Semeen Rehman, and Muhammad Shafique. 2018. Robust Machine Learning Systems: Reliability and Security for Deep Neural Networks. In 2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS). 257--260."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_9_1","volume-title":"Flipcy: Efficient Pattern Redistribution for Enhancing MLC PCM Reliability and Storage Density. In 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). 1--7.","author":"Imran Muhammad","year":"2019","unstructured":"Muhammad Imran, Taehyun Kwon, et al. 2019. Flipcy: Efficient Pattern Redistribution for Enhancing MLC PCM Reliability and Storage Density. In 2019 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). 1--7."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE48585.2020.9116188"},{"key":"e_1_3_2_1_11_1","volume-title":"Simon Haefeli, Irem Boybat, Sasidharan Rajalekshmi Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, and Evangelos Eleftheriou.","author":"Joshi Vinay","year":"2020","unstructured":"Vinay Joshi, Manuel Le Gallo, Simon Haefeli, Irem Boybat, Sasidharan Rajalekshmi Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, and Evangelos Eleftheriou. 2020. Accurate deep neural network inference using computational phase-change memory. Nature communications 11, 1 (2020), 1--13."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317805"},{"key":"e_1_3_2_1_13_1","volume-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper. CoRR abs\/1806.08342","author":"Krishnamoorthi Raghuraman","year":"2018","unstructured":"Raghuraman Krishnamoorthi. 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. CoRR abs\/1806.08342 (2018). arXiv:1806.08342 http:\/\/arxiv.org\/abs\/1806.08342"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.3009498"},{"key":"e_1_3_2_1_15_1","volume-title":"Automation Test in Europe Conference Exhibition (DATE). 1610--1615","author":"Kwon Taehyun","year":"2018","unstructured":"Taehyun Kwon, Muhammad Imran, Jung Min You, and Joon-Sung Yang. 2018. Heterogeneous PCM array architecture for reliability, performance and lifetime enhancement. In 2018 Design, Automation Test in Europe Conference Exhibition (DATE). 1610--1615."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3126908.3126964"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/dac18074.2021.9586112"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465834"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485922.2485960"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001139"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2020.2971217"},{"key":"e_1_3_2_1_22_1","volume-title":"Drift-Invariant Detection for Multilevel Phase-Change Memory. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). 1--5.","author":"Stanisavljevic Milos","year":"2018","unstructured":"Milos Stanisavljevic, Thomas Mittelholzer, Nikolaos Papandreou, Thomas Parnell, and Haralampos Pozidis. 2018. Drift-Invariant Detection for Multilevel Phase-Change Memory. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). 1--5."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/imw.2016.7495263"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218585"},{"key":"e_1_3_2_1_25_1","volume-title":"Deep Learning and Unsupervised Feature Learning Workshop, NIPS","author":"Vanhoucke Vincent","year":"2011","unstructured":"Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. 2011. Improving the speed of neural networks on CPUs. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2016.27"},{"key":"e_1_3_2_1_27_1","volume-title":"Fully hardware-implemented memristor convolutional neural network. Nature 577, 7792","author":"Yao Peng","year":"2020","unstructured":"Peng Yao, Huaqiang Wu, Bin Gao, Jianshi Tang, Qingtian Zhang, Wenqiang Zhang, J. Joshua Yang, and He Qian. 2020. Fully hardware-implemented memristor convolutional neural network. Nature 577, 7792 (2020), 641--646."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317866"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSSC.2016.2546199"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2011.5958219"}],"event":{"name":"ICCAD '22: IEEE\/ACM International Conference on Computer-Aided Design","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-EDS Electronic Devices Society","IEEE CAS","IEEE CEDA"],"location":"San Diego California","acronym":"ICCAD '22"},"container-title":["Proceedings of the 41st IEEE\/ACM International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549400","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508352.3549400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:57Z","timestamp":1750186977000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549400"}},"subtitle":["A Dynamic Pattern-Aware Encoding Technique for Robust MLC PCM-Based Deep Neural Networks"],"short-title":[],"issued":{"date-parts":[[2022,10,30]]},"references-count":30,"alternative-id":["10.1145\/3508352.3549400","10.1145\/3508352"],"URL":"https:\/\/doi.org\/10.1145\/3508352.3549400","relation":{},"subject":[],"published":{"date-parts":[[2022,10,30]]},"assertion":[{"value":"2022-12-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}