{"id":"https://openalex.org/W4312090609","doi":"https://doi.org/10.48550/arxiv.2212.09975","title":"Sophisticated deep learning with on-chip optical diffractive tensor processing","display_name":"Sophisticated deep learning with on-chip optical diffractive tensor processing","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312090609","doi":"https://doi.org/10.48550/arxiv.2212.09975"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.09975","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2212.09975","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102712077","display_name":"Yuyao Huang","orcid":"https://orcid.org/0000-0002-3989-9514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yuyao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006519315","display_name":"Tingzhao Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Tingzhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035927623","display_name":"Honghao Huang","orcid":"https://orcid.org/0000-0002-3487-1790"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Honghao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086225982","display_name":"Sigang Yang","orcid":"https://orcid.org/0000-0002-2209-287X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Sigang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324853","display_name":"Hongwei Chen","orcid":"https://orcid.org/0000-0002-2952-2203"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongwei","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.640014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":70},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9996,"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":0.9996,"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":0.9973,"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/T10232","display_name":"Optical Network Technologies","score":0.9966,"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/mnist-database","display_name":"MNIST database","score":0.50053096},{"id":"https://openalex.org/keywords/graphics-processing-unit","display_name":"Graphics processing unit","score":0.42757225}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.789011},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7373383},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6772987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.63387334},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.50053096},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4817624},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4669645},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.43032596},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.42757225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38910374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35550195},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3292146},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.32770404},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.28456593},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11627394},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.108929664}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.09975","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2212.09975","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2212.09975","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.85,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2327123731","https://openalex.org/W2168586703","https://openalex.org/W2163816448","https://openalex.org/W2095734710","https://openalex.org/W2028469001","https://openalex.org/W2027201655","https://openalex.org/W2009665355","https://openalex.org/W1965860946","https://openalex.org/W185782823","https://openalex.org/W1656096860"],"abstract_inverted_index":{"The":[0,185],"ever-growing":[1],"deep":[2,33,125,225],"learning":[3,34,126],"technologies":[4],"are":[5,16,137],"making":[6],"revolutionary":[7],"changes":[8],"for":[9,215],"modern":[10],"life.":[11],"However,":[12],"conventional":[13],"computing":[14,68,217],"architectures":[15],"designed":[17],"to":[18,43,74,121,157,200,219],"process":[19],"sequential":[20],"and":[21,31,47,59,129,134,143,182,195,205],"digital":[22],"programs,":[23],"being":[24],"extremely":[25],"burdened":[26],"with":[27,95,139,165,176],"performing":[28],"massive":[29],"parallel":[30,211],"adaptive":[32],"applications.":[35],"Photonic":[36],"integrated":[37],"circuits":[38],"provide":[39],"an":[40,66,115,150],"efficient":[41],"approach":[42],"mitigate":[44],"bandwidth":[45],"limitations":[46],"power-wall":[48],"brought":[49],"by":[50,71,93],"its":[51,201],"electronic":[52],"counterparts,":[53],"showing":[54],"great":[55],"potential":[56],"in":[57,161,224],"ultrafast":[58],"energy-free":[60],"high-performance":[61],"computing.":[62],"Here,":[63],"we":[64,113,148],"propose":[65],"optical":[67,79,116,151],"architecture":[69,218],"enabled":[70],"on-chip":[72],"diffraction":[73],"implement":[75,122],"convolutional":[76,117,153],"acceleration,":[77],"termed":[78],"convolution":[80,88],"unit":[81],"(OCU).":[82],"We":[83],"demonstrate":[84],"that":[85],"any":[86],"real-valued":[87],"kernels":[89],"can":[90],"be":[91],"exploited":[92],"OCU":[94,108,187],"a":[96,209],"prominent":[97],"computational":[98],"throughput":[99],"boosting":[100],"via":[101],"the":[102,110],"concept":[103],"of":[104,141,179,191],"structral":[105],"re-parameterization.":[106],"With":[107],"as":[109],"fundamental":[111],"unit,":[112],"build":[114,149],"neural":[118,154],"network":[119,155],"(oCNN)":[120],"two":[123],"popular":[124],"tasks:":[127],"classification":[128],"regression.":[130],"For":[131,146],"classification,":[132],"Fashion-MNIST":[133],"CIFAR-4":[135],"datasets":[136],"tested":[138],"accuracy":[140],"91.63%":[142],"86.25%,":[144],"respectively.":[145,184],"regression,":[147],"denoising":[152],"(oDnCNN)":[156],"handle":[158,220],"Gaussian":[159],"noise":[160,166],"gray":[162],"scale":[163],"images":[164,175],"level":[167],"{\\sigma}":[168],"=":[169],"10,":[170],"15,":[171],"20,":[172],"resulting":[173],"clean":[174],"average":[177],"PSNR":[178],"31.70dB,":[180],"29.39dB":[181],"27.72dB,":[183],"proposed":[186],"presents":[188],"remarkable":[189],"performance":[190],"low":[192],"energy":[193],"consumption":[194],"high":[196,221],"information":[197],"density":[198],"due":[199],"fully":[202],"passive":[203],"nature":[204],"compact":[206],"footprint,":[207],"providing":[208],"highly":[210],"while":[212],"lightweight":[213],"solution":[214],"future":[216],"dimensional":[222],"tensors":[223],"learning.":[226]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4312090609","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-04T17:07:26.386831","created_date":"2023-01-04"}