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정홍식

Jeong, Hongsik
Future Semiconductor Technology Lab.
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dc.citation.endPage 393 -
dc.citation.startPage 390 -
dc.citation.title IEEE SOLID-STATE CIRCUITS LETTERS -
dc.citation.volume 3 -
dc.contributor.author Lee, Kyoung-Rog -
dc.contributor.author Kim, Jihoon -
dc.contributor.author Kim, Changhyeon -
dc.contributor.author Han, Donghyeon -
dc.contributor.author Lee, Juhyoung -
dc.contributor.author Lee, Jinsu -
dc.contributor.author Jeong, Hongsik -
dc.contributor.author Yoo, Hoi-Jun -
dc.date.accessioned 2023-12-21T16:46:25Z -
dc.date.available 2023-12-21T16:46:25Z -
dc.date.created 2023-09-05 -
dc.date.issued 2020-10 -
dc.description.abstract A low-power STT-MRAM-based mixed-mode electrocardiogram (ECG) arrhythmia monitoring SoC is proposed. The proposed SoC consists of 1-MB STT-MRAM, leakage-based delay multiply-and-accumulation (MAC) unit (LDMAC), and ECG analog front end (AFE). ResNet structure with 16 1-D convolution layers and max-pooling layers is adopted for the ECG arrhythmia detection with weight reusing and partial sum reusing scheme. A nonvolatile 1-MB STT-MRAM enables deep neural network (DNN) inference to achieve higher area efficiency, lower power consumption without external memory access. The proposed mixed-mode LDMAC consumes only 4.11-nW MAC power by reusing leakage current. The proposed SoC is fabricated in 28-nm FDSOI process with 7.29-mm2 area. It demonstrates ECG arrhythmia detection with 85.1% accuracy, which is the highest score reported, and the lowest power consumption of 1.02 μW. © 2018 IEEE. -
dc.identifier.bibliographicCitation IEEE SOLID-STATE CIRCUITS LETTERS, v.3, pp.390 - 393 -
dc.identifier.doi 10.1109/LSSC.2020.3024622 -
dc.identifier.issn 2573-9603 -
dc.identifier.scopusid 2-s2.0-85091274539 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/65351 -
dc.language 영어 -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title A 1.02-μW STT-MRAM-Based DNN ECG arrhythmia monitoring SoC with leakage-based delay MAC unit -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.type.docType Article -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Biomedical deep neural network (DNN) -
dc.subject.keywordAuthor DNN SoC -
dc.subject.keywordAuthor electrocardiogram arrhythmia -
dc.subject.keywordAuthor mixed-mode multiply-and-accumulation -
dc.subject.keywordAuthor STT-MRAM -

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