{"id":100149,"date":"2025-08-29T04:51:26","date_gmt":"2025-08-29T04:51:26","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/100149\/"},"modified":"2025-08-29T04:51:26","modified_gmt":"2025-08-29T04:51:26","slug":"diffractive-tensorized-unit-for-million-tops-general-purpose-computing","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/100149\/","title":{"rendered":"Diffractive tensorized unit for million-TOPS general-purpose computing"},"content":{"rendered":"<p class=\"c-article-references__text\" id=\"ref-CR1\">Jaeger, H., Noheda, B. &amp; van der Wiel, W. G. Toward a formal theory for computing machines made out of whatever physics offers. Nat. Commun. 14, 4911 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NatCo..14.4911J\" aria-label=\"ADS reference 1\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 1\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Toward%20a%20formal%20theory%20for%20computing%20machines%20made%20out%20of%20whatever%20physics%20offers&amp;journal=Nat.%20Commun.&amp;volume=14&amp;publication_year=2023&amp;author=Jaeger%2CH&amp;author=Noheda%2CB&amp;author=Wiel%2CWG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Brunner, D. &amp; Psaltis, D. Competitive photonic neural networks. Nat. Photonics 15, 323\u2013324 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021NaPho..15..323B\" aria-label=\"ADS reference 2\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 2\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Competitive%20photonic%20neural%20networks&amp;journal=Nat.%20Photonics&amp;volume=15&amp;pages=323-324&amp;publication_year=2021&amp;author=Brunner%2CD&amp;author=Psaltis%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Huang, C. et al. Prospects and applications of photonic neural networks. Adv. Phys. X 7, 1981155 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 3\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Prospects%20and%20applications%20of%20photonic%20neural%20networks&amp;journal=Adv.%20Phys.%20X&amp;volume=7&amp;publication_year=2022&amp;author=Huang%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Fang, L. et al. Engram-driven videography. Engineering 25, 101\u2013109 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 4\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Engram-driven%20videography&amp;journal=Engineering&amp;volume=25&amp;pages=101-109&amp;publication_year=2023&amp;author=Fang%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">McMahon, P. L. The physics of optical computing. Nat. Rev. Phys. 5, 717\u2013734 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 5\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20physics%20of%20optical%20computing&amp;journal=Nat.%20Rev.%20Phys.&amp;volume=5&amp;pages=717-734&amp;publication_year=2023&amp;author=McMahon%2CPL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102\u2013114 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021NaPho..15..102S\" aria-label=\"ADS reference 6\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 6\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Photonics%20for%20artificial%20intelligence%20and%20neuromorphic%20computing&amp;journal=Nat.%20Photon.&amp;volume=15&amp;pages=102-114&amp;publication_year=2021&amp;author=Shastri%2CBJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">Xue, Z. et al. Fully forward mode training for optical neural networks. Nature 632, 280\u2013286 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 7\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fully%20forward%20mode%20training%20for%20optical%20neural%20networks&amp;journal=Nature&amp;volume=632&amp;pages=280-286&amp;publication_year=2024&amp;author=Xue%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441\u2013446 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2017NaPho..11..441S\" aria-label=\"ADS reference 8\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 8\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20learning%20with%20coherent%20nanophotonic%20circuits&amp;journal=Nat.%20Photon.&amp;volume=11&amp;pages=441-446&amp;publication_year=2017&amp;author=Shen%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Meng, X. et al. Compact optical convolution processing unit based on multimode interference. Nat. Commun. 14, 3000 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NatCo..14.3000M\" aria-label=\"ADS reference 9\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 9\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Compact%20optical%20convolution%20processing%20unit%20based%20on%20multimode%20interference&amp;journal=Nat.%20Commun.&amp;volume=14&amp;publication_year=2023&amp;author=Meng%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52\u201358 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021Natur.589...52F\" aria-label=\"ADS reference 10\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 10\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Parallel%20convolutional%20processing%20using%20an%20integrated%20photonic%20tensor%20core&amp;journal=Nature&amp;volume=589&amp;pages=52-58&amp;publication_year=2021&amp;author=Feldmann%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Ashtiani, F., Geers, A. J. &amp; Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606, 501\u2013506 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022Natur.606..501A\" aria-label=\"ADS reference 11\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 11\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=An%20on-chip%20photonic%20deep%20neural%20network%20for%20image%20classification&amp;journal=Nature&amp;volume=606&amp;pages=501-506&amp;publication_year=2022&amp;author=Ashtiani%2CF&amp;author=Geers%2CAJ&amp;author=Aflatouni%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Fyrillas, A., Faure, O., Maring, N., Senellart, J. &amp; Belabas, N. Scalable machine learning-assisted clear-box characterization for optimally controlled photonic circuits. Optica 11, 427 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024Optic..11..427F\" aria-label=\"ADS reference 12\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 12\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Scalable%20machine%20learning-assisted%20clear-box%20characterization%20for%20optimally%20controlled%20photonic%20circuits&amp;journal=Optica&amp;volume=11&amp;publication_year=2024&amp;author=Fyrillas%2CA&amp;author=Faure%2CO&amp;author=Maring%2CN&amp;author=Senellart%2CJ&amp;author=Belabas%2CN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39\u201347 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2020Natur.588...39W\" aria-label=\"ADS reference 13\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 13\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Inference%20in%20artificial%20intelligence%20with%20deep%20optics%20and%20photonics&amp;journal=Nature&amp;volume=588&amp;pages=39-47&amp;publication_year=2020&amp;author=Wetzstein%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Zhou, H. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11, 30 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022LSA....11...30Z\" aria-label=\"ADS reference 14\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 14\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Photonic%20matrix%20multiplication%20lights%20up%20photonic%20accelerator%20and%20beyond&amp;journal=Light%20Sci.%20Appl.&amp;volume=11&amp;publication_year=2022&amp;author=Zhou%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004\u20131008 (2018).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2018Sci...361.1004L\" aria-label=\"ADS reference 15\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=3837095\" aria-label=\"MathSciNet reference 15\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 15\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=All-optical%20machine%20learning%20using%20diffractive%20deep%20neural%20networks&amp;journal=Science&amp;volume=361&amp;pages=1004-1008&amp;publication_year=2018&amp;author=Lin%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367\u2013373 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021NaPho..15..367Z\" aria-label=\"ADS reference 16\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 16\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20neuromorphic%20optoelectronic%20computing%20with%20a%20reconfigurable%20diffractive%20processing%20unit&amp;journal=Nat.%20Photon.&amp;volume=15&amp;pages=367-373&amp;publication_year=2021&amp;author=Zhou%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768\u2013775 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023Natur.620..768A\" aria-label=\"ADS reference 17\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 17\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=An%20analog-AI%20chip%20for%20energy-efficient%20speech%20recognition%20and%20transcription&amp;journal=Nature&amp;volume=620&amp;pages=768-775&amp;publication_year=2023&amp;author=Ambrogio%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Liu, C. et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 5, 113\u2013122 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 18\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20programmable%20diffractive%20deep%20neural%20network%20based%20on%20a%20digital-coding%20metasurface%20array&amp;journal=Nat.%20Electron.&amp;volume=5&amp;pages=113-122&amp;publication_year=2022&amp;author=Liu%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Wu, T., Menarini, M., Gao, Z. &amp; Feng, L. Lithography-free reconfigurable integrated photonic processor. Nat. Photonics 17, 710\u2013716 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NaPho..17..710W\" aria-label=\"ADS reference 19\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 19\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Lithography-free%20reconfigurable%20integrated%20photonic%20processor&amp;journal=Nat.%20Photonics&amp;volume=17&amp;pages=710-716&amp;publication_year=2023&amp;author=Wu%2CT&amp;author=Menarini%2CM&amp;author=Gao%2CZ&amp;author=Feng%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Zuo, C. &amp; Chen, Q. Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks. Light Sci. Appl. 11, 208 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022LSA....11..208Z\" aria-label=\"ADS reference 20\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 20\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Exploiting%20optical%20degrees%20of%20freedom%20for%20information%20multiplexing%20in%20diffractive%20neural%20networks&amp;journal=Light%20Sci.%20Appl.&amp;volume=11&amp;publication_year=2022&amp;author=Zuo%2CC&amp;author=Chen%2CQ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Zhang, Z. et al. Space\u2013time projection enabled ultrafast all\u2010optical diffractive neural network. Laser Photon. Rev. 18, 2301367 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024LPRv...1801363Z\" aria-label=\"ADS reference 21\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 21\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Space%E2%80%93time%20projection%20enabled%20ultrafast%20all%E2%80%90optical%20diffractive%20neural%20network&amp;journal=Laser%20Photon.%20Rev.&amp;volume=18&amp;publication_year=2024&amp;author=Zhang%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl. 8, 112 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2019LSA.....8..112L\" aria-label=\"ADS reference 22\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 22\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Design%20of%20task-specific%20optical%20systems%20using%20broadband%20diffractive%20neural%20networks&amp;journal=Light%20Sci.%20Appl.&amp;volume=8&amp;publication_year=2019&amp;author=Luo%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Luo, X. et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci. Appl. 11, 158 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022LSA....11..158L\" aria-label=\"ADS reference 23\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 23\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Metasurface-enabled%20on-chip%20multiplexed%20diffractive%20neural%20networks%20in%20the%20visible&amp;journal=Light%20Sci.%20Appl.&amp;volume=11&amp;publication_year=2022&amp;author=Luo%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Kulce, O., Mengu, D., Rivenson, Y. &amp; Ozcan, A. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl. 10, 25 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 24\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=All-optical%20information-processing%20capacity%20of%20diffractive%20surfaces&amp;journal=Light%20Sci.%20Appl&amp;volume=10&amp;publication_year=2021&amp;author=Kulce%2CO&amp;author=Mengu%2CD&amp;author=Rivenson%2CY&amp;author=Ozcan%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Hu, J. et al. Diffractive optical computing in free space. Nat. Commun. 15, 1525 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024NatCo..15.1525H\" aria-label=\"ADS reference 25\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 25\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Diffractive%20optical%20computing%20in%20free%20space&amp;journal=Nat.%20Commun.&amp;volume=15&amp;publication_year=2024&amp;author=Hu%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Rahman, M. S. S., Yang, X., Li, J., Bai, B. &amp; Ozcan, A. Universal linear intensity transformations using spatially incoherent diffractive processors. Light Sci. Appl. 12, 195 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023LSA....12..195R\" aria-label=\"ADS reference 26\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 26\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Universal%20linear%20intensity%20transformations%20using%20spatially%20incoherent%20diffractive%20processors&amp;journal=Light%20Sci.%20Appl.&amp;volume=12&amp;publication_year=2023&amp;author=Rahman%2CMSS&amp;author=Yang%2CX&amp;author=Li%2CJ&amp;author=Bai%2CB&amp;author=Ozcan%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Kulce, O., Mengu, D., Rivenson, Y. &amp; Ozcan, A. All-optical synthesis of an arbitrary linear transformation using diffractive surfaces. Light Sci. Appl. 10, 196 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021LSA....10..196K\" aria-label=\"ADS reference 27\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 27\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=All-optical%20synthesis%20of%20an%20arbitrary%20linear%20transformation%20using%20diffractive%20surfaces&amp;journal=Light%20Sci.%20Appl.&amp;volume=10&amp;publication_year=2021&amp;author=Kulce%2CO&amp;author=Mengu%2CD&amp;author=Rivenson%2CY&amp;author=Ozcan%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">Cheng, Y. et al. Photonic neuromorphic architecture for tens-of-task lifelong learning. Light Sci. Appl. 13, 56 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024LSA....13...56C\" aria-label=\"ADS reference 28\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 28\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Photonic%20neuromorphic%20architecture%20for%20tens-of-task%20lifelong%20learning&amp;journal=Light%20Sci.%20Appl.&amp;volume=13&amp;publication_year=2024&amp;author=Cheng%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">Xu, Z. et al. Large-scale photonic chiplet Taichi empowers 160-TOPS\/W artificial general intelligence. Science 384, 202\u2013209 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024Sci...384..202X\" aria-label=\"ADS reference 29\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 29\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20photonic%20chiplet%20Taichi%20empowers%20160-TOPS%2FW%20artificial%20general%20intelligence&amp;journal=Science&amp;volume=384&amp;pages=202-209&amp;publication_year=2024&amp;author=Xu%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">Gu, T., Kim, H. J., Rivero-Baleine, C. &amp; Hu, J. Reconfigurable metasurfaces towards commercial success. Nat. Photon. 17, 48\u201358 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NaPho..17...48G\" aria-label=\"ADS reference 30\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 30\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Reconfigurable%20metasurfaces%20towards%20commercial%20success&amp;journal=Nat.%20Photon.&amp;volume=17&amp;pages=48-58&amp;publication_year=2023&amp;author=Gu%2CT&amp;author=Kim%2CHJ&amp;author=Rivero-Baleine%2CC&amp;author=Hu%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Yao, Y., Wei, Y., Dong, J., Li, M. &amp; Zhang, X. Large-scale reconfigurable integrated circuits for wideband analog photonic computing. Photonics 10, 300 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 31\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20reconfigurable%20integrated%20circuits%20for%20wideband%20analog%20photonic%20computing&amp;journal=Photonics&amp;volume=10&amp;publication_year=2023&amp;author=Yao%2CY&amp;author=Wei%2CY&amp;author=Dong%2CJ&amp;author=Li%2CM&amp;author=Zhang%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">Nemati, A., Wang, Q., Hong, M. H. &amp; Teng, J. H. Tunable and reconfigurable metasurfaces and metadevices. Opto-Electron. Adv. 1, 1\u201325 (2018).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 32\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Tunable%20and%20reconfigurable%20metasurfaces%20and%20metadevices&amp;journal=Opto-Electron.%20Adv.&amp;volume=1&amp;pages=1-25&amp;publication_year=2018&amp;author=Nemati%2CA&amp;author=Wang%2CQ&amp;author=Hong%2CMH&amp;author=Teng%2CJH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR33\">Qu, Y., Lian, H., Ding, C., Liu, H. &amp; Liu, L. High-frame-rate reconfigurable diffractive neural network based on superpixels. Opt. Lett 48, 1\u20134 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023OptL...48....1H\" aria-label=\"ADS reference 33\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 33\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=High-frame-rate%20reconfigurable%20diffractive%20neural%20network%20based%20on%20superpixels&amp;journal=Opt.%20Lett&amp;volume=48&amp;pages=1-4&amp;publication_year=2023&amp;author=Qu%2CY&amp;author=Lian%2CH&amp;author=Ding%2CC&amp;author=Liu%2CH&amp;author=Liu%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">Yang, G. et al. Nonlocal phase-change metaoptics for reconfigurable nonvolatile image processing. Light Sci. Appl. 14, 182 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 34\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Nonlocal%20phase-change%20metaoptics%20for%20reconfigurable%20nonvolatile%20image%20processing&amp;journal=Light%20Sci.%20Appl.&amp;volume=14&amp;publication_year=2025&amp;author=Yang%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">Dinsdale, N. J. et al. Deep learning enabled design of complex transmission matrices for universal optical components. ACS Photonics 8, 283\u2013295 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 35\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20learning%20enabled%20design%20of%20complex%20transmission%20matrices%20for%20universal%20optical%20components&amp;journal=ACS%20Photonics&amp;volume=8&amp;pages=283-295&amp;publication_year=2021&amp;author=Dinsdale%2CNJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Li, Q., Sun, Y. &amp; Zhang, X. Single-layer universal optical computing. Phys. Rev. A 109, 053527 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024PhRvA.109e3527L\" aria-label=\"ADS reference 36\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 36\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Single-layer%20universal%20optical%20computing&amp;journal=Phys.%20Rev.%20A&amp;volume=109&amp;publication_year=2024&amp;author=Li%2CQ&amp;author=Sun%2CY&amp;author=Zhang%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Giamougiannis, G. et al. A coherent photonic crossbar for scalable universal linear optics. J. Light. Technol. 41, 2425\u20132442 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023JLwT...41.2425G\" aria-label=\"ADS reference 37\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 37\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20coherent%20photonic%20crossbar%20for%20scalable%20universal%20linear%20optics&amp;journal=J.%20Light.%20Technol.&amp;volume=41&amp;pages=2425-2442&amp;publication_year=2023&amp;author=Giamougiannis%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Yang, Y., Krompass, D. &amp; Tresp, V. Tensor-train recurrent neural networks for video classification. In Proc. 34th International Conference on Machine Learning <a href=\"https:\/\/proceedings.mlr.press\/v70\/yang17e\/yang17e.pdf\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/proceedings.mlr.press\/v70\/yang17e\/yang17e.pdf\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/proceedings.mlr.press\/v70\/yang17e\/yang17e.pdf<\/a> (PMLR, 2017).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Cheng, Y., Li, G., Wong, N., Chen, H. &amp; Yu, H. DEEPEYE: a deeply tensor-compressed neural network for video comprehension on terminal devices. ACM Trans. Embed. Comput. Syst. 19, 1\u201325 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 39\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=DEEPEYE%3A%20a%20deeply%20tensor-compressed%20neural%20network%20for%20video%20comprehension%20on%20terminal%20devices&amp;journal=ACM%20Trans.%20Embed.%20Comput.%20Syst.&amp;volume=19&amp;pages=1-25&amp;publication_year=2020&amp;author=Cheng%2CY&amp;author=Li%2CG&amp;author=Wong%2CN&amp;author=Chen%2CH&amp;author=Yu%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Miscuglio, M. &amp; Sorger, V. J. Photonic tensor cores for machine learning. Appl. Phys. Rev. 7, 031404 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2020ApPRv...7c1404M\" aria-label=\"ADS reference 40\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 40\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Photonic%20tensor%20cores%20for%20machine%20learning&amp;journal=Appl.%20Phys.%20Rev.&amp;volume=7&amp;publication_year=2020&amp;author=Miscuglio%2CM&amp;author=Sorger%2CVJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Wang, Y. et al. An energy-efficient nonvolatile in-memory computing architecture for extreme learning machine by domain-wall nanowire devices. IEEE Trans. Nanotechnol. 14, 998\u20131012 (2015).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2015ITNan..14..998W\" aria-label=\"ADS reference 41\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 41\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=An%20energy-efficient%20nonvolatile%20in-memory%20computing%20architecture%20for%20extreme%20learning%20machine%20by%20domain-wall%20nanowire%20devices&amp;journal=IEEE%20Trans.%20Nanotechnol.&amp;volume=14&amp;pages=998-1012&amp;publication_year=2015&amp;author=Wang%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR42\">Cheng, Y., Wang, C., Chen, H.-B. &amp; Yu, H. A large-scale in-memory computing for deep neural network with trained quantization. Integration 69, 345\u2013355 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 42\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20large-scale%20in-memory%20computing%20for%20deep%20neural%20network%20with%20trained%20quantization&amp;journal=Integration&amp;volume=69&amp;pages=345-355&amp;publication_year=2019&amp;author=Cheng%2CY&amp;author=Wang%2CC&amp;author=Chen%2CH-B&amp;author=Yu%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR43\">Krizhevsky, A. et al. Learning multiple layers of features from tiny images. University of Toronto <a href=\"https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf<\/a> (2009).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR44\">Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248-255 (IEEE, 2009); <a href=\"https:\/\/doi.org\/10.1109\/CVPR.2009.5206848\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1109\/CVPR.2009.5206848\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1109\/CVPR.2009.5206848<\/a><\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR45\">Oseledets, I. V. Tensor-train decomposition. SIAM J. Sci. Comput. 33, 2295\u20132317 (2011).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=2837533\" aria-label=\"MathSciNet reference 45\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 45\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Tensor-train%20decomposition&amp;journal=SIAM%20J.%20Sci.%20Comput.&amp;volume=33&amp;pages=2295-2317&amp;publication_year=2011&amp;author=Oseledets%2CIV\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR46\">Cheng, Y., Yang, Y., Chen, H.-B., Wong, N. &amp; Yu, H. S3-Net: a fast scene understanding network by single-shot segmentation for autonomous driving. ACM Trans. Intell. Syst. Technol. 12, 1\u201319 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 46\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=S3-Net%3A%20a%20fast%20scene%20understanding%20network%20by%20single-shot%20segmentation%20for%20autonomous%20driving&amp;journal=ACM%20Trans.%20Intell.%20Syst.%20Technol.&amp;volume=12&amp;pages=1-19&amp;publication_year=2021&amp;author=Cheng%2CY&amp;author=Yang%2CY&amp;author=Chen%2CH-B&amp;author=Wong%2CN&amp;author=Yu%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR47\">A, de S.-E. The Little Prince and Letter to a Hostage (Penguin UK, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR48\">Rong, X. word2vec parameter learning explained. Nature 606, 501\u2013506 (2014).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 48\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=word2vec%20parameter%20learning%20explained&amp;journal=Nature&amp;volume=606&amp;pages=501-506&amp;publication_year=2014&amp;author=Rong%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR49\">Graves, A., Jaitly, N. &amp; Mohamed, A. Hybrid speech recognition with Deep Bidirectional LSTM. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding 273\u2013278 (IEEE, 2013); <a href=\"https:\/\/doi.org\/10.1109\/ASRU.2013.6707742\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1109\/ASRU.2013.6707742\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ASRU.2013.6707742<\/a><\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR50\">Gesmundo, A. &amp; Dean, J. An evolutionary approach to dynamic introduction of tasks in large-scale multitask learning systems. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2205.12755\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2205.12755\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2205.12755<\/a> (2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR51\">Plath, J., Sinclair, G. &amp; Curnutt, K. The 100 Greatest Literary Characters (Bloomsbury, 2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR52\">Carroll L. Alice\u2019s Adventures in Wonderland (Broadview Press, 2011).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR53\">Baum, L. F. The Wonderful Wizard of Oz (Broadview Press, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR54\">Abdi, H. &amp; Williams, L. J. Principal component analysis. WIREs Comput. Stat. 2, 433\u2013459 (2010).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 54\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Principal%20component%20analysis&amp;journal=WIREs%20Comput.%20Stat.&amp;volume=2&amp;pages=433-459&amp;publication_year=2010&amp;author=Abdi%2CH&amp;author=Williams%2CLJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR55\">He, K., Zhang, X., Ren, S. &amp; Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770\u2013778 (IEEE, 2016).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR56\">Wang, B. Dataset for couplets. GitHub <a href=\"https:\/\/github.com\/wb14123\/couplet-dataset\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/github.com\/wb14123\/couplet-dataset\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/github.com\/wb14123\/couplet-dataset<\/a> (2018).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR57\">michaelarman. Poems Dataset (NLP). Kaggle <a href=\"https:\/\/www.kaggle.com\/datasets\/michaelarman\/poemsdataset\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.kaggle.com\/datasets\/michaelarman\/poemsdataset\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.kaggle.com\/datasets\/michaelarman\/poemsdataset<\/a> (2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR58\">Karvelis, P., Gavrilis, D., Georgoulas, G. &amp; Stylios, C. Topic recommendation using Doc2Vec. In 2018 International Joint Conference on Neural Networks (IJCNN) 1\u20136 (IEEE, 2018); <a href=\"https:\/\/doi.org\/10.1109\/IJCNN.2018.8489513\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1109\/IJCNN.2018.8489513\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1109\/IJCNN.2018.8489513<\/a><\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR59\">Chen, D. &amp; Dollan, W. Collecting highly parallel data for paraphrase evaluation. In Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (eds Lin, D. et al.) 190\u2013200 (Association for Computational Linguistics, 2011).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR60\">Abu-El-Haija, S. et al. YouTube-8M: a large-scale video classification benchmark. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1609.08675\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/1609.08675\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/1609.08675<\/a> (2016).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR61\">Yang, A. et al. Vid2Seq: large-scale pretraining of a visual language model for dense video captioning. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2302.14115\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2302.14115\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2302.14115<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR62\">Liang, Y., Zhu, L., Wang, X. &amp; Yang, Y. IcoCap: improving video captioning by compounding images. IEEE Trans. Multimed. 26, 4389\u20134400 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 62\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=IcoCap%3A%20improving%20video%20captioning%20by%20compounding%20images&amp;journal=IEEE%20Trans.%20Multimed.&amp;volume=26&amp;pages=4389-4400&amp;publication_year=2024&amp;author=Liang%2CY&amp;author=Zhu%2CL&amp;author=Wang%2CX&amp;author=Yang%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR63\">Xu, J., Mei, T., Yao, T. &amp; Rui, Y. MSR-VTT: a large video description dataset for bridging video and language. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5288\u20135296 (IEEE, 2016); <a href=\"https:\/\/doi.org\/10.1109\/CVPR.2016.571\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1109\/CVPR.2016.571\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1109\/CVPR.2016.571<\/a><\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR64\">Schuldt, C., Laptev, I. &amp; Caputo, B. Recognizing human actions: a local SVM approach. In Proc. 17th International Conference on Pattern Recognition, ICPR 2004 <a href=\"https:\/\/doi.org\/10.1109\/ICPR.2004.1334462\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1109\/ICPR.2004.1334462\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1109\/ICPR.2004.1334462<\/a> (IEEE, 2004).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR65\">Srivastava, N., Mansimov, E. &amp; Salakhutdinov, R. Unsupervised learning of video representations using LSTMs. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1502.04681\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/1502.04681\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/1502.04681<\/a> (2015).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR66\">Lecun, Y., Bottou, L., Bengio, Y. &amp; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 66\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Gradient-based%20learning%20applied%20to%20document%20recognition&amp;journal=Proc.%20IEEE&amp;volume=86&amp;pages=2278-2324&amp;publication_year=1998&amp;author=Lecun%2CY&amp;author=Bottou%2CL&amp;author=Bengio%2CY&amp;author=Haffner%2CP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR67\">Wang, C. et al. Diffractive tensorized unit for million-TOPS general-purpose computing. Dryad <a href=\"https:\/\/doi.org\/10.5061\/dryad.7d7wm387c\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.5061\/dryad.7d7wm387c\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.5061\/dryad.7d7wm387c<\/a> (2025).<\/p>\n","protected":false},"excerpt":{"rendered":"Jaeger, H., Noheda, B. &amp; van der Wiel, W. G. 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