Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/94272
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dc.contributorDepartment of Biomedical Engineering-
dc.contributorMainland Development Office-
dc.contributorPhotonics Research Institute-
dc.creatorLi, H-
dc.creatorYu, Z-
dc.creatorZhao, Q-
dc.creatorZhong, T-
dc.creatorLai, P-
dc.date.accessioned2022-08-11T02:01:33Z-
dc.date.available2022-08-11T02:01:33Z-
dc.identifier.urihttp://hdl.handle.net/10397/94272-
dc.language.isoenen_US
dc.publisherCell Pressen_US
dc.rights© 2022 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Li, H., Yu, Z., Zhao, Q., Zhong, T., & Lai, P. (2022). Accelerating deep learning with high energy efficiency: From microchip to physical systems. The Innovation, 3(4), 100252 is available at https://doi.org/10.1016/j.xinn.2022.100252en_US
dc.titleAccelerating deep learning with high energy efficiency : from microchip to physical systemsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume3-
dc.identifier.issue4-
dc.identifier.doi10.1016/j.xinn.2022.100252-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe innovation, 12 July 2022, v. 3, no. 4, 100252-
dcterms.isPartOfThe innovation-
dcterms.issued2022-07-
dc.identifier.scopus2-s2.0-85129969755-
dc.identifier.eissn2666-6758-
dc.identifier.artn100252-
dc.description.validate202208 bckw-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera1563en_US
dc.identifier.SubFormID45430en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; Guangdong Science and Technology Commission; Hong Kong Innovation and Technology Commissionen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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