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dc.contributor.authorHan Hye Rim-
dc.contributor.authorJo, Jeonghee-
dc.contributor.author박종길-
dc.contributor.authorPark, Seongsik-
dc.date.accessioned2024-01-12T02:46:07Z-
dc.date.available2024-01-12T02:46:07Z-
dc.date.created2023-10-30-
dc.date.issued2023-06-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/76434-
dc.identifier.urihttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11522582-
dc.description.abstractMultimodal models have been studied as promising model that can overcome the disadvantages of the unimodal models. However, not much research has been conducted on the effectiveness of multimodality in spiking neural networks (SNNs), which have been considered a next-generation artificial neural network for their energy efficiency. Thus, in this paper, we analyzed the effectiveness through experiments on modality, model size, and noise. According to our analysis, we validated that SNNs showed greater effectiveness in multimodality than DNNs.-
dc.languageKorean-
dc.publisher(사)대한전자공학회-
dc.title멀티모달 스파이킹 뉴럴 네트워크의 성능 분석-
dc.title.alternativePerformance Analysis of Multi-Modal Spiking Neural Networks-
dc.typeConference-
dc.description.journalClass2-
dc.identifier.bibliographicCitation2023년도 대한전자공학회 하계종합학술대회-
dc.citation.title2023년도 대한전자공학회 하계종합학술대회-
dc.citation.conferencePlaceKO-
dc.citation.conferencePlace제주도-
dc.citation.conferenceDate2023-06-28-
dc.relation.isPartOf2023년도 대한전자공학회 하계학술대회 논문집-

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