{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:13:48Z","timestamp":1774624428797,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T00:00:00Z","timestamp":1525046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ministry of Science and ICT","award":["NRF-2016M3A7B4910249"],"award-info":[{"award-number":["NRF-2016M3A7B4910249"]}]},{"name":"ICT Consilience Creative Program","award":["IITP-2017-R0346-16-1007"],"award-info":[{"award-number":["IITP-2017-R0346-16-1007"]}]},{"name":"Ministry of Trade, Industry 8 Energy"},{"name":"\u201cNano-Material Technology Development Program\u201d"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Industrial Technology Innovation Program","award":["10067764"],"award-info":[{"award-number":["10067764"]}]},{"name":"MSIT (Ministry of Science and ICT), Korea"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2018,4,30]]},"abstract":"<jats:p>Artificial Neural Network computation relies on intensive vector-matrix multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array showed a feasibility of implementing such operations with high energy efficiency. Thus, there have been many works on efficiently utilizing emerging NVM crossbar arrays as analog vector-matrix multipliers. However, nonlinear I-V characteristics of NVM restrain critical design parameters, such as the read voltage and weight range, resulting in substantial accuracy loss. In this article, instead of optimizing hardware parameters to a given neural network, we propose a methodology of reconstructing the neural network itself to be optimized to resistive memory crossbar arrays. To verify the validity of the proposed method, we simulated various neural networks with MNIST and CIFAR-10 dataset using two different Resistive Random Access Memory models. Simulation results show that our proposed neural network produces inference accuracies significantly higher than conventional neural network when the network is mapped to synapse devices with nonlinear I-V characteristics.<\/jats:p>","DOI":"10.1145\/3145478","type":"journal-article","created":{"date-parts":[[2018,7,12]],"date-time":"2018-07-12T15:38:47Z","timestamp":1531409927000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics"],"prefix":"10.1145","volume":"14","author":[{"given":"Hyungjun","family":"Kim","sequence":"first","affiliation":[{"name":"POSTECH, South Korea, Gyeongsangbukdo, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taesu","family":"Kim","sequence":"additional","affiliation":[{"name":"POSTECH, South Korea, Gyeongsangbukdo, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinseok","family":"Kim","sequence":"additional","affiliation":[{"name":"POSTECH, South Korea, Gyeongsangbukdo, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae-Joon","family":"Kim","sequence":"additional","affiliation":[{"name":"POSTECH, South Korea, Gyeongsangbukdo, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7,12]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"89","article-title":"Neuromorphic computing using non-volatile memory","author":"Burr Geoffrey W.","year":"2017","unstructured":"Geoffrey W. 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In Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. Retrieved from http:\/\/hal.archives-ouvertes.fr\/inria-00112631\/. Kumar Chellapilla, S. Puri, and Patrice Simard. 2006. High performance convolutional neural networks for document processing. In Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. Retrieved from http:\/\/hal.archives-ouvertes.fr\/inria-00112631\/."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2755753.2755947"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.13"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2012.2231683"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1039\/C4NR06406B"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Tayfun Gokmen and Yurii Vlasov. 2016. Acceleration of deep neural network training with resistive cross-point devices: Design considerations. Front. 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