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dc.contributor.authorRíos, C
dc.contributor.authorYoungblood, N
dc.contributor.authorCheng, Z
dc.contributor.authorLe Gallo, M
dc.contributor.authorPernice, WHP
dc.contributor.authorWright, CD
dc.contributor.authorSebastian, A
dc.contributor.authorBhaskaran, H
dc.date.accessioned2019-03-11T15:19:22Z
dc.date.issued2019-02
dc.description.abstractCollocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge2Sb2Te5, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG)en_GB
dc.description.sponsorshipEuropean Research Council (ERC)en_GB
dc.identifier.citationVol. 5, eaau5759en_GB
dc.identifier.doi10.1126/sciadv.aau5759
dc.identifier.grantnumberEP/J018694/1en_GB
dc.identifier.grantnumberEP/M015173/1en_GB
dc.identifier.grantnumberEP/M015130/1en_GB
dc.identifier.grantnumberPE 1832/2-1en_GB
dc.identifier.grantnumber682675en_GB
dc.identifier.otheraau5759
dc.identifier.urihttp://hdl.handle.net/10871/36393
dc.language.isoenen_GB
dc.publisherAmerican Association for the Advancement of Science:en_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/30793028en_GB
dc.rightsCopyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY)en_GB
dc.titleIn-memory computing on a photonic platformen_GB
dc.typeArticleen_GB
dc.date.available2019-03-11T15:19:22Z
exeter.place-of-publicationUnited Statesen_GB
dc.descriptionThis is the final version. Available from the publisher via the DOI in this record.en_GB
dc.descriptionAll data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors or Oxford Research Archive for Data (https://ora.ox.ac.uk).en_GB
dc.identifier.journalScience Advancesen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2019-01-07
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2019-02
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2019-03-11T15:13:57Z
refterms.versionFCDVoR
refterms.dateFOA2019-03-11T15:19:24Z
refterms.panelBen_GB


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Copyright © 2019
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
License 4.0 (CC BY)
Except where otherwise noted, this item's licence is described as Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY)