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Energy-Efficient AI at the edge for Biomedical Applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Jerald | - |
dc.date.accessioned | 2024-05-03T04:30:20Z | - |
dc.date.available | 2024-05-03T04:30:20Z | - |
dc.date.created | 2024-05-03 | - |
dc.date.created | 2024-05-03 | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Proceedings - International SoC Design Conference 2023, ISOCC 2023, pp.202-202 | - |
dc.identifier.issn | 2163-9612 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200774 | - |
dc.description.abstract | This paper presents a AI-on-the-edge System-on-Chip (SoC) for biomedical applications. For ambulatory tracking and effective treatment of neurological disorders such as seizure and epilepsy, long-term monitoring wearable SoCs is essential to 'close the loop'. To satisfy the wearable form factor, the design challenges at techniques of feature extraction and classification to improve seizure detection accuracy at the Digital Back-End (DBE) must be addressed at a system perspective. Furthermore, future trends of the epilepsy tracking system are discussed. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Energy-Efficient AI at the edge for Biomedical Applications | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ISOCC59558.2023.10396488 | - |
dc.citation.journaltitle | Proceedings - International SoC Design Conference 2023, ISOCC 2023 | - |
dc.identifier.wosid | 001169439300096 | - |
dc.identifier.scopusid | 2-s2.0-85184815851 | - |
dc.citation.endpage | 202 | - |
dc.citation.startpage | 202 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoo, Jerald | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | AI-on-the-edge | - |
dc.subject.keywordAuthor | ambulatory | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | deep | - |
dc.subject.keywordAuthor | learning | - |
dc.subject.keywordAuthor | epilepsy tracking | - |
dc.subject.keywordAuthor | feature extraction | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | online tuning | - |
dc.subject.keywordAuthor | patient-specific | - |
dc.subject.keywordAuthor | scalability | - |
dc.subject.keywordAuthor | wearable | - |
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- Department of Electrical and Computer Engineering
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