{"id":"https://openalex.org/W7204263624","doi":"https://doi.org/10.1117/12.3099919","title":"Integrated, reconfigurable photonic tensor processor for DNN inference","display_name":"Integrated, reconfigurable photonic tensor processor for DNN inference","publication_year":2026,"publication_date":"2026-08-26","ids":{"openalex":"https://openalex.org/W7204263624","doi":"https://doi.org/10.1117/12.3099919"},"language":"en","primary_location":{"id":"doi:10.1117/12.3099919","doi":"https://doi.org/10.1117/12.3099919","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3099919","pdf_url":null,"source":{"id":"https://openalex.org/S7407106955","display_name":"SPIE Machine Learning in Photonics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning in Photonics II","raw_type":"proceedings-article"},"volume":null,"type":"conference-abstract","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120081355","display_name":"Simon Tebeck","orcid":"https://orcid.org/0009-0009-8397-8680","observed_orcids":["https://orcid.org/0009-0009-8397-8680"]},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Simon Tebeck","raw_affiliation_strings":["Ruprecht-Karls-Univ. Heidelberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruprecht-Karls-Univ. Heidelberg (Germany)","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113691924","display_name":"Lennart Meyer","orcid":"https://orcid.org/0009-0000-9381-9856","observed_orcids":["https://orcid.org/0009-0000-9381-9856"]},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lennart Meyer","raw_affiliation_strings":["Ruprecht-Karls-Univ. Heidelberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruprecht-Karls-Univ. Heidelberg (Germany)","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049824986","display_name":"Jelle Dijkstra","orcid":"https://orcid.org/0009-0007-4647-4235","observed_orcids":["https://orcid.org/0009-0007-4647-4235"]},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jelle Dijkstra","raw_affiliation_strings":["Ruprecht-Karls-Univ. Heidelberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruprecht-Karls-Univ. Heidelberg (Germany)","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015151220","display_name":"Wolfram H. P. Pernice","orcid":"https://orcid.org/0000-0003-4569-4213","observed_orcids":["https://orcid.org/0000-0003-4569-4213"]},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfram Pernice","raw_affiliation_strings":["Ruprecht-Karls-Univ. Heidelberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruprecht-Karls-Univ. Heidelberg (Germany)","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039132958","display_name":"Frank Br\u00fcckerhoff\u2010Pl\u00fcckelmann","orcid":"https://orcid.org/0009-0009-7713-6735","observed_orcids":["https://orcid.org/0009-0009-7713-6735","https://orcid.org/0009-0006-5748-7069"]},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Frank Br\u00fcckerhoff-Pl\u00fcckelmann","raw_affiliation_strings":["Ruprecht-Karls-Univ. Heidelberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruprecht-Karls-Univ. Heidelberg (Germany)","institution_ids":["https://openalex.org/I223822909"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I223822909"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10299","display_name":"Photonic and Optical Devices","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-neural-network-inference","display_name":"deep neural network inference","score":0.8519999980926514},{"id":"https://openalex.org/keywords/optical-computing","display_name":"optical computing","score":0.5379999876022339},{"id":"https://openalex.org/keywords/silicon-photonics","display_name":"silicon photonics","score":0.8560000061988831},{"id":"https://openalex.org/keywords/reconfigurable-photonic-integrated-circuits","display_name":"reconfigurable photonic integrated circuits","score":0.8069999814033508},{"id":"https://openalex.org/keywords/electroabsorption-modulator","display_name":"electroabsorption modulator","score":0.8949999809265137},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.5389999747276306},{"id":"https://openalex.org/keywords/mnist","display_name":"MNIST","score":0.656000018119812},{"id":"https://openalex.org/keywords/cifar-10","display_name":"CIFAR-10","score":1.0}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6255000233650208},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4034000039100647},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.35440000891685486},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C20788544","wikidata":"https://www.wikidata.org/wiki/Q467054","display_name":"Photonics","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.3099919","doi":"https://doi.org/10.1117/12.3099919","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3099919","pdf_url":null,"source":{"id":"https://openalex.org/S7407106955","display_name":"SPIE Machine Learning in Photonics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning in Photonics II","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7152647782","https://openalex.org/W4385315986","https://openalex.org/W4290714069","https://openalex.org/W7133483814","https://openalex.org/W4404905249","https://openalex.org/W4324326983","https://openalex.org/W3092231822","https://openalex.org/W4412161579","https://openalex.org/W4403586871","https://openalex.org/W4401359414"],"abstract_inverted_index":{"We":[0],"present":[1],"an":[2,33],"integrated,":[3],"reconfigurable":[4],"photonic":[5,79,104],"tensor":[6],"processor":[7],"(PTP)":[8],"that":[9],"performs":[10],"deep-neural-network":[11],"inference":[12],"in":[13,32],"the":[14,24,83],"optical":[15,35],"domain.":[16],"Fabricated":[17],"on":[18,94,98],"imec\u2019s":[19],"iSiPP50G":[20],"silicon":[21],"photonics":[22],"platform,":[23],"chip":[25],"combines":[26],"electro-absorption":[27],"modulators":[28],"and":[29,37,86,96],"on-chip":[30],"photodiodes":[31],"incoherent":[34],"crossbar":[36],"is":[38],"packaged":[39],"as":[40],"a":[41,46],"19-inch":[42],"rack":[43],"unit":[44],"with":[45],"high-speed":[47],"FPGA":[48],"interface":[49],"for":[50,63],"seamless":[51],"PyTorch":[52],"deployment.":[53],"An":[54],"integrated":[55,80],"self-injection-locked":[56],"Si\u200bN":[57],"microcomb":[58],"supplies":[59],"stable":[60],"multi-wavelength":[61],"carriers":[62],"parallel,":[64],"single-cycle":[65],"multiply\u2013accumulate":[66],"operations.The":[67],"rack-mounted":[68],"package":[69],"exposes":[70],"standard":[71],"electrical":[72],"interfaces,":[73],"facilitating":[74],"drop-in":[75],"evaluation":[76],"of":[77],"other":[78],"circuits":[81],"using":[82],"same":[84],"control":[85],"readout":[87],"stack.":[88],"The":[89],"system":[90],"achieves":[91],"98.1%":[92],"accuracy":[93],"MNIST":[95],"72.0%":[97],"CIFAR-10,":[99],"demonstrating":[100],"practical,":[101],"reprogrammable,":[102],"low-latency":[103],"AI":[105],"processing.":[106]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-27T00:00:00"}
