DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Wooseok | - |
dc.contributor.author | Ji, Wonjae | - |
dc.contributor.author | Heo, Seongjae | - |
dc.contributor.author | Lee, Donguk | - |
dc.contributor.author | Noh, Kyungmi | - |
dc.contributor.author | Lee, Chuljun | - |
dc.contributor.author | Woo, Jiyong | - |
dc.contributor.author | Kim, Seyoung | - |
dc.contributor.author | Hwang, Hyunsang | - |
dc.date.accessioned | 2023-03-03T00:50:37Z | - |
dc.date.available | 2023-03-03T00:50:37Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 0741-3106 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/116424 | - |
dc.description.abstract | Conductance variations of resistive random-access memory (RRAM) are significant challenges that hinder the accurate inference of neural network (NN) hardware. In this study, we exploit the read noise of the RRAM as an active computational enabler for implementing probabilistic NN. As electrical characteristics of RRAM are directly related to the properties of conductive filament (CF), we statistically explore read current of TiOx-based RRAM with different forming conditions and explain the results by linking the CF model. In addition, an array mapping scheme to transfer weights to one transistor-one RRAM (1T1R) array is experimentally demonstrated. Through NN simulations, we verify that the probabilistic NN shows promising results on nonlinear classification problem avoiding overconfidence compared with deterministic NN. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE ELECTRON DEVICE LETTERS | - |
dc.title | Exploiting Read Current Noise of TiOx Resistive Memory by Controlling Forming Conditions for Probabilistic Neural Network Hardware | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LED.2022.3192262 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE ELECTRON DEVICE LETTERS, v.43, no.9, pp.1571 - 1574 | - |
dc.identifier.wosid | 000845067200049 | - |
dc.citation.endPage | 1574 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 1571 | - |
dc.citation.title | IEEE ELECTRON DEVICE LETTERS | - |
dc.citation.volume | 43 | - |
dc.contributor.affiliatedAuthor | Choi, Wooseok | - |
dc.contributor.affiliatedAuthor | Ji, Wonjae | - |
dc.contributor.affiliatedAuthor | Heo, Seongjae | - |
dc.contributor.affiliatedAuthor | Lee, Donguk | - |
dc.contributor.affiliatedAuthor | Noh, Kyungmi | - |
dc.contributor.affiliatedAuthor | Lee, Chuljun | - |
dc.contributor.affiliatedAuthor | Kim, Seyoung | - |
dc.contributor.affiliatedAuthor | Hwang, Hyunsang | - |
dc.identifier.scopusid | 2-s2.0-85135228651 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | LEVEL | - |
dc.subject.keywordAuthor | Artificial neural networks | - |
dc.subject.keywordAuthor | Temperature measurement | - |
dc.subject.keywordAuthor | Probabilistic logic | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Temperature distribution | - |
dc.subject.keywordAuthor | Standards | - |
dc.subject.keywordAuthor | Switches | - |
dc.subject.keywordAuthor | Bayesian neural networks | - |
dc.subject.keywordAuthor | filamentary RRAM | - |
dc.subject.keywordAuthor | neuromorphic | - |
dc.subject.keywordAuthor | probabilistic computing | - |
dc.subject.keywordAuthor | synaptic device | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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