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In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is proposed. Furthermore, alternate approaches of mapping the synaptic weights into fully trained and semi-trained crossbars are investigated. In a semi-trained crossbar, a confined subset of memristors are tuned and the remaining subset of memristors are not programmed. This translates to optimal resource utilization and power consumption, compared to a fully programmed crossbar. The semi-trained crossbar architecture is applicable to a broad class of neural networks. System level verification is performed with an extreme learning machine for binomial and multinomial classification. The total power for a single 4 \u00d7 4 layer network, when implemented in IBM 65nm node, is estimated to be \u224842.16\u03bcW and the area is estimated to be 26.48\u03bcm \u00d7 22.35\u03bcm.<\/jats:p>","DOI":"10.1145\/3233987","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T13:18:59Z","timestamp":1543324739000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Semi-Trained Memristive Crossbar Computing Engine with\n            <i>In Situ<\/i>\n            Learning Accelerator"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8220-5285","authenticated-orcid":false,"given":"Abdullah M.","family":"Zyarah","sequence":"first","affiliation":[{"name":"Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhireesha","family":"Kudithipudi","sequence":"additional","affiliation":[{"name":"Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,27]]},"reference":[{"volume-title":"Strukov","year":"2013","author":"Alibart Fabien","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.7551\/978-0-262-32621-6-ch076"},{"volume-title":"Memristive switches enable stateful logic operations via material implication. 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