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Comput. Eng."],"published-print":{"date-parts":[[2023,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices\u2019 retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.<\/jats:p>","DOI":"10.1088\/2634-4386\/acb965","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T22:33:52Z","timestamp":1675722832000},"page":"014011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior"],"prefix":"10.1088","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6346-4422","authenticated-orcid":true,"given":"Yibei","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2732-3419","authenticated-orcid":true,"given":"Qingtian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Wenbin","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5027-7938","authenticated-orcid":true,"given":"Yue","family":"Xi","sequence":"additional","affiliation":[]},{"given":"Zhixing","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jianshi","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2417-983X","authenticated-orcid":true,"given":"Bin","family":"Gao","sequence":"additional","affiliation":[]},{"given":"He","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Huaqiang","family":"Wu","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"nceacb965bib1","first-page":"494","article-title":"A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors","author":"Chen","year":"2018"},{"key":"nceacb965bib2","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/JPROC.2018.2790840","article-title":"Neuro-inspired computing with emerging nonvolatile memorys","volume":"106","author":"Yu","year":"2018","journal-title":"Proc. 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