An integrated photonics engine for unsupervised correlation detection.
dc.contributor.author | Ghazi Sarwat, S | |
dc.contributor.author | Brückerhoff-Plückelmann, F | |
dc.contributor.author | Carrillo, SG-C | |
dc.contributor.author | Gemo, E | |
dc.contributor.author | Feldmann, J | |
dc.contributor.author | Bhaskaran, H | |
dc.contributor.author | Wright, CD | |
dc.contributor.author | Pernice, WHP | |
dc.contributor.author | Sebastian, A | |
dc.date.accessioned | 2022-07-27T08:27:14Z | |
dc.date.issued | 2022-06-01 | |
dc.date.updated | 2022-07-26T16:31:32Z | |
dc.description.abstract | With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge2Sb2Te5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods. | en_GB |
dc.description.sponsorship | European Union’s Horizon 2020 | en_GB |
dc.description.sponsorship | European Union’s Horizon 2020 | en_GB |
dc.description.sponsorship | European Union’s Horizon 2020 | en_GB |
dc.format.extent | eabn3243- | |
dc.format.medium | Print-Electronic | |
dc.identifier.citation | Vol. 8, No. 22, article eabn3243 | en_GB |
dc.identifier.doi | https://doi.org/10.1126/sciadv.abn3243 | |
dc.identifier.grantnumber | 780848 | en_GB |
dc.identifier.grantnumber | 101017237 | en_GB |
dc.identifier.grantnumber | 899598 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/130401 | |
dc.identifier | ORCID: 0000-0001-8349-6627 (Gemo, Emanuele) | |
dc.identifier | ScopusID: 57191043612 (Gemo, Emanuele) | |
dc.identifier | ORCID: 0000-0003-4087-7467 (Wright, C David) | |
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/35648858 | en_GB |
dc.rights | Copyright © 2022 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 | An integrated photonics engine for unsupervised correlation detection. | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-07-27T08:27:14Z | |
dc.identifier.issn | 2375-2548 | |
exeter.article-number | ARTN eabn3243 | |
exeter.place-of-publication | United States | |
dc.description | This is the final version. Available from the American Association for the Advancement of Science via the DOI in this record. | en_GB |
dc.description | Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. | en_GB |
dc.identifier.journal | Science Advances | en_GB |
dc.relation.ispartof | Sci Adv, 8(22) | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-04-15 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-06-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-07-27T08:21:20Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-07-27T08:27:16Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2022-06-01 |
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Except where otherwise noted, this item's licence is described as Copyright © 2022 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).