{"id":"https://openalex.org/W1922123711","doi":"https://doi.org/10.48550/arxiv.1312.5851","title":"Fast Training of Convolutional Networks through FFTs","display_name":"Fast Training of Convolutional Networks through FFTs","publication_year":2013,"publication_date":"2013-12-20","ids":{"openalex":"https://openalex.org/W1922123711","doi":"https://doi.org/10.48550/arxiv.1312.5851","mag":"1922123711"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1312.5851","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1312.5851","pdf_url":"https://arxiv.org/pdf/1312.5851","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1312.5851","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016135148","display_name":"Micha\u00ebl Mathieu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mathieu, Michael","raw_affiliation_strings":["Courant Institute of Mathematical Sciences, New York University#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Courant Institute of Mathematical Sciences, New York University#TAB#","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045766292","display_name":"Mikael Henaff","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henaff, Mikael","raw_affiliation_strings":["Courant Institute of Mathematical Sciences, New York University#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Courant Institute of Mathematical Sciences, New York University#TAB#","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001226970","display_name":"Yann LeCun","orcid":"https://orcid.org/0000-0002-1992-2684","observed_orcids":["https://orcid.org/0000-0002-1992-2684"]},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"LeCun, Yann","raw_affiliation_strings":["Courant Institute of Mathematical Sciences, New York University#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Courant Institute of Mathematical Sciences, New York University#TAB#","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":273,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9164999723434448,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9164999723434448,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.07240000367164612,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.001500000013038516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9160000085830688},{"id":"https://openalex.org/keywords/training-acceleration","display_name":"training acceleration","score":0.8379999995231628},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"fast Fourier transform","score":0.609000027179718},{"id":"https://openalex.org/keywords/gpu-implementation","display_name":"GPU implementation","score":0.6110000014305115},{"id":"https://openalex.org/keywords/inference-acceleration","display_name":"inference acceleration","score":0.9150000214576721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8147709369659424},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7066532373428345},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.60283362865448},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.5410493612289429},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5310372114181519},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49689581990242004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4956594705581665},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4766148626804352},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4738484025001526},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4297516345977783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40829405188560486},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3741229772567749},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.12464827299118042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0732414722442627},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1312.5851","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1312.5851","pdf_url":"https://arxiv.org/pdf/1312.5851","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1312.5851","doi":"https://doi.org/10.48550/arxiv.1312.5851","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1312.5851","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1312.5851","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1312.5851","pdf_url":"https://arxiv.org/pdf/1312.5851","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W753012316","https://openalex.org/W1556219185","https://openalex.org/W1598732512","https://openalex.org/W2061171222","https://openalex.org/W2108598243","https://openalex.org/W2154683974","https://openalex.org/W2155904486"],"related_works":["https://openalex.org/W7100467770","https://openalex.org/W2777685882","https://openalex.org/W2255738257","https://openalex.org/W1789336918","https://openalex.org/W2909967491","https://openalex.org/W4283119136","https://openalex.org/W2949245006","https://openalex.org/W2911583028","https://openalex.org/W2976463866","https://openalex.org/W2509104353"],"abstract_inverted_index":{"Convolutional":[0],"networks":[1],"are":[2,30],"one":[3],"of":[4,28,87,91,132],"the":[5,108,113],"most":[6],"widely":[7],"employed":[8],"architectures":[9],"in":[10,107],"computer":[11],"vision":[12],"and":[13,77,83,128],"machine":[14],"learning.":[15],"In":[16,66],"order":[17,90],"to":[18,22,39,94],"leverage":[19],"their":[20],"ability":[21],"learn":[23],"complex":[24],"functions,":[25],"large":[26,36],"amounts":[27],"data":[29],"required":[31],"for":[32],"training.":[33],"Training":[34],"a":[35,54,71,80,125,130],"convolutional":[37],"network":[38,56],"produce":[40],"state-of-the-art":[41,96],"results":[42],"can":[43,57,84],"take":[44],"weeks,":[45],"even":[46],"when":[47,61],"using":[48,53],"modern":[49],"GPUs.":[50],"Producing":[51],"labels":[52],"trained":[55],"also":[58],"be":[59],"costly":[60],"dealing":[62],"with":[63],"web-scale":[64],"datasets.":[65],"this":[67],"work,":[68],"we":[69],"present":[70],"simple":[72],"algorithm":[73,121],"which":[74],"accelerates":[75],"training":[76],"inference":[78],"by":[79,101],"significant":[81],"factor,":[82],"yield":[85],"improvements":[86],"over":[88],"an":[89],"magnitude":[92],"compared":[93],"existing":[95],"implementations.":[97],"This":[98],"is":[99,122],"done":[100],"computing":[102],"convolutions":[103],"as":[104],"pointwise":[105],"products":[106],"Fourier":[109],"domain":[110],"while":[111],"reusing":[112],"same":[114],"transformed":[115],"feature":[116],"map":[117],"many":[118],"times.":[119],"The":[120],"implemented":[123],"on":[124],"GPU":[126],"architecture":[127],"addresses":[129],"number":[131],"related":[133],"challenges.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":43},{"year":2020,"cited_by_count":41},{"year":2019,"cited_by_count":33},{"year":2018,"cited_by_count":35},{"year":2017,"cited_by_count":33},{"year":2016,"cited_by_count":28},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":10}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2016-06-24T00:00:00"}