In-memory computing on a photonic platform
dc.contributor.author | Ríos, C | |
dc.contributor.author | Youngblood, N | |
dc.contributor.author | Cheng, Z | |
dc.contributor.author | Le Gallo, M | |
dc.contributor.author | Pernice, WHP | |
dc.contributor.author | Wright, CD | |
dc.contributor.author | Sebastian, A | |
dc.contributor.author | Bhaskaran, H | |
dc.date.accessioned | 2019-03-11T15:19:22Z | |
dc.date.issued | 2019-02 | |
dc.description.abstract | Collocated 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.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Deutsche Forschungsgemeinschaft (DFG) | en_GB |
dc.description.sponsorship | European Research Council (ERC) | en_GB |
dc.identifier.citation | Vol. 5, eaau5759 | en_GB |
dc.identifier.doi | 10.1126/sciadv.aau5759 | |
dc.identifier.grantnumber | EP/J018694/1 | en_GB |
dc.identifier.grantnumber | EP/M015173/1 | en_GB |
dc.identifier.grantnumber | EP/M015130/1 | en_GB |
dc.identifier.grantnumber | PE 1832/2-1 | en_GB |
dc.identifier.grantnumber | 682675 | en_GB |
dc.identifier.other | aau5759 | |
dc.identifier.uri | http://hdl.handle.net/10871/36393 | |
dc.language.iso | en | en_GB |
dc.publisher | American Association for the Advancement of Science: | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/30793028 | en_GB |
dc.rights | 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) | en_GB |
dc.title | In-memory computing on a photonic platform | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-03-11T15:19:22Z | |
exeter.place-of-publication | United States | en_GB |
dc.description | This is the final version. Available from the publisher via the DOI in this record. | en_GB |
dc.description | All 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.journal | Science Advances | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-01-07 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-02 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-03-11T15:13:57Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-03-11T15:19:24Z | |
refterms.panel | B | en_GB |
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