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Energy-Efficient AI at the edge for Biomedical Applications

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dc.contributor.authorYoo, Jerald-
dc.date.accessioned2024-05-03T04:30:20Z-
dc.date.available2024-05-03T04:30:20Z-
dc.date.created2024-05-03-
dc.date.created2024-05-03-
dc.date.issued2023-
dc.identifier.citationProceedings - International SoC Design Conference 2023, ISOCC 2023, pp.202-202-
dc.identifier.issn2163-9612-
dc.identifier.urihttps://hdl.handle.net/10371/200774-
dc.description.abstractThis 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.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleEnergy-Efficient AI at the edge for Biomedical Applications-
dc.typeArticle-
dc.identifier.doi10.1109/ISOCC59558.2023.10396488-
dc.citation.journaltitleProceedings - International SoC Design Conference 2023, ISOCC 2023-
dc.identifier.wosid001169439300096-
dc.identifier.scopusid2-s2.0-85184815851-
dc.citation.endpage202-
dc.citation.startpage202-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorYoo, Jerald-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorAI-on-the-edge-
dc.subject.keywordAuthorambulatory-
dc.subject.keywordAuthorclassification-
dc.subject.keywordAuthordeep-
dc.subject.keywordAuthorlearning-
dc.subject.keywordAuthorepilepsy tracking-
dc.subject.keywordAuthorfeature extraction-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthoronline tuning-
dc.subject.keywordAuthorpatient-specific-
dc.subject.keywordAuthorscalability-
dc.subject.keywordAuthorwearable-
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부교수
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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