DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kyoung-Rog | ko |
dc.contributor.author | Kim, Jihoon | ko |
dc.contributor.author | Kim, Changhyeon | ko |
dc.contributor.author | Han, Donghyeon | ko |
dc.contributor.author | Lee, Juhyoung | ko |
dc.contributor.author | Lee, Jinsu | ko |
dc.contributor.author | Jeong, Hongsik | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.date.accessioned | 2021-03-05T01:10:04Z | - |
dc.date.available | 2021-03-05T01:10:04Z | - |
dc.date.created | 2021-03-04 | - |
dc.date.created | 2021-03-04 | - |
dc.date.created | 2021-03-04 | - |
dc.date.created | 2021-03-04 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.citation | IEEE SOLID-STATE CIRCUITS LETTERS, v.3, pp.390 - 393 | - |
dc.identifier.issn | 2573-9603 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281230 | - |
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. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL 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.identifier.scopusid | 2-s2.0-85091274539 | - |
dc.type.rims | ART | - |
dc.citation.volume | 3 | - |
dc.citation.beginningpage | 390 | - |
dc.citation.endingpage | 393 | - |
dc.citation.publicationname | IEEE SOLID-STATE CIRCUITS LETTERS | - |
dc.identifier.doi | 10.1109/LSSC.2020.3024622 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Kim, Jihoon | - |
dc.contributor.nonIdAuthor | Lee, Jinsu | - |
dc.contributor.nonIdAuthor | Jeong, Hongsik | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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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