{"id":"https://openalex.org/W4293102204","doi":"https://doi.org/10.1145/3524069","title":"Winograd Convolution for Deep Neural Networks: Efficient Point Selection","display_name":"Winograd Convolution for Deep Neural Networks: Efficient Point Selection","publication_year":2022,"publication_date":"2022-03-21","ids":{"openalex":"https://openalex.org/W4293102204","doi":"https://doi.org/10.1145/3524069"},"language":"en","primary_location":{"id":"doi:10.1145/3524069","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524069","pdf_url":null,"source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"},"type":"article","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/A5061182145","display_name":"Syed Asad Alam","orcid":"https://orcid.org/0000-0002-1509-9678"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Syed Asad Alam","raw_affiliation_strings":["Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-1509-9678","affiliations":[{"raw_affiliation_string":"Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010687192","display_name":"Andrew Anderson","orcid":"https://orcid.org/0000-0002-4357-4739"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Andrew Anderson","raw_affiliation_strings":["Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-4357-4739","affiliations":[{"raw_affiliation_string":"Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083322811","display_name":"Barbara Barabasz","orcid":"https://orcid.org/0000-0001-7303-2511"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Barbara Barabasz","raw_affiliation_strings":["Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0001-7303-2511","affiliations":[{"raw_affiliation_string":"Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003800161","display_name":"David Gregg","orcid":"https://orcid.org/0000-0003-3782-4612"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"David Gregg","raw_affiliation_strings":["Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland"],"raw_orcid":"https://orcid.org/0000-0003-3782-4612","affiliations":[{"raw_affiliation_string":"Lero, Trinity College Dublin, the University of Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I205274468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205274468"],"apc_list":null,"apc_paid":null,"fwci":2.2513,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.89466183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"21","issue":"6","first_page":"1","last_page":"28"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9902999997138977,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9833999872207642,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7931519746780396},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.670810878276825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6688092947006226},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5390177965164185},{"id":"https://openalex.org/keywords/overlap\u2013add-method","display_name":"Overlap\u2013add method","score":0.5291992425918579},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5189014077186584},{"id":"https://openalex.org/keywords/convolution-theorem","display_name":"Convolution theorem","score":0.46484583616256714},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4461619555950165},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4259328246116638},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4241899847984314},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.31636863946914673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3024136424064636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2980293035507202},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.11408683657646179}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7931519746780396},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.670810878276825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6688092947006226},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5390177965164185},{"id":"https://openalex.org/C181002996","wikidata":"https://www.wikidata.org/wiki/Q1611641","display_name":"Overlap\u2013add method","level":5,"score":0.5291992425918579},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5189014077186584},{"id":"https://openalex.org/C79587385","wikidata":"https://www.wikidata.org/wiki/Q2638931","display_name":"Convolution theorem","level":5,"score":0.46484583616256714},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4461619555950165},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4259328246116638},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4241899847984314},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31636863946914673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3024136424064636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2980293035507202},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.11408683657646179},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.0},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3524069","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524069","pdf_url":null,"source":{"id":"https://openalex.org/S136160450","display_name":"ACM Transactions on Embedded Computing Systems","issn_l":"1539-9087","issn":["1539-9087","1558-3465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Embedded Computing Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8515955051","display_name":"Lero_Phase 2","funder_award_id":"13/RC/2094_P2","funder_id":"https://openalex.org/F4320320847","funder_display_name":"Science Foundation Ireland"}],"funders":[{"id":"https://openalex.org/F4320320847","display_name":"Science Foundation Ireland","ror":"https://ror.org/0271asj38"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W565312106","https://openalex.org/W1487564550","https://openalex.org/W1501488688","https://openalex.org/W1582291092","https://openalex.org/W2061681121","https://openalex.org/W2073389244","https://openalex.org/W2078114900","https://openalex.org/W2097117768","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2202151333","https://openalex.org/W2279098554","https://openalex.org/W2416799949","https://openalex.org/W2568772110","https://openalex.org/W2618530766","https://openalex.org/W2627042741","https://openalex.org/W2742152118","https://openalex.org/W2773281122","https://openalex.org/W2897748868","https://openalex.org/W2960833983","https://openalex.org/W2997109118","https://openalex.org/W3011121930","https://openalex.org/W3098382995","https://openalex.org/W3111688502","https://openalex.org/W3118608800","https://openalex.org/W4235199656","https://openalex.org/W4237812669","https://openalex.org/W4296980820","https://openalex.org/W6787029084"],"related_works":["https://openalex.org/W4363675452","https://openalex.org/W2267589039","https://openalex.org/W2293685972","https://openalex.org/W4372260258","https://openalex.org/W2919798019","https://openalex.org/W2388359778","https://openalex.org/W2133280289","https://openalex.org/W2078931192","https://openalex.org/W2056497345","https://openalex.org/W2145522134"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1,315],"networks":[2,316],"(CNNs)":[3],"have":[4],"dramatically":[5],"improved":[6],"the":[7,41,57,105,109,112,148,153,172,184,196,207,274],"accuracy":[8,107],"of":[9,31,60,85,93,102,108,115,147,156,166,189,198,209,247,273,287],"image,":[10],"video,":[11],"and":[12,23,37,128,232,252,266,307,320],"audio":[13],"processing":[14],"for":[15,34,117,159,186,249,263,349],"tasks":[16],"such":[17,50,70],"as":[18,51,131,229],"object":[19],"recognition,":[20],"image":[21],"segmentation,":[22],"interactive":[24],"speech":[25],"systems.":[26],"CNNs":[27],"require":[28],"large":[29],"amounts":[30],"computing":[32],"resources":[33],"both":[35,264],"training":[36],"inference,":[38],"primarily":[39],"because":[40],"convolution":[42,48,53,65,88,305,314],"layers":[43],"are":[44,98],"computationally":[45],"intensive.":[46],"Fast":[47],"algorithms":[49],"Winograd":[52,64,87,173,304,341],"can":[54,236,344],"greatly":[55],"reduce":[56,177],"computational":[58],"cost":[59],"these":[61],"layers.":[62],"However,":[63],"has":[66],"poor":[67],"numeric":[68,106,178],"properties,":[69],"that":[71,164,176,183,211,218,233,291,322],"greater":[72],"savings":[73],"in":[74,171,255,280,328,346],"computation":[75],"cause":[76,169],"exponentially":[77],"increasing":[78],"floating":[79],"point":[80,143],"errors.":[81],"A":[82],"defining":[83],"feature":[84],"each":[86],"algorithm":[89],"is":[90,202,220],"a":[91,139,192,245,285,296,302],"set":[92,114],"real-value":[94,199],"points":[95,103,116,146,290,325],"where":[96],"polynomials":[97],"sampled.":[99],"The":[100],"choice":[101],"impacts":[104],"algorithm,":[110],"but":[111],"optimal":[113],"small":[118,126,250],"convolutions":[119,251],"remains":[120],"unknown.":[121],"Existing":[122],"work":[123],"considers":[124],"only":[125],"integers":[127,225],"simple":[129,227],"fractions":[130,228],"candidate":[132],"points.":[133,242],"In":[134],"this":[135,167],"work,":[136],"we":[137,277,283,300],"propose":[138],"novel":[140],"approach":[141],"to":[142,205,213,223,260,271,295,310,333],"selection":[144],"using":[145,152],"form":[149,168],"\\(\\lbrace":[150],"-\\frac{1}{c},-c,c,\\frac{1}{c}\\rbrace\\)":[151],"full":[154],"range":[155,197,246],"real-valued":[157,241],"numbers":[158],"c":[160,190,210],".":[161],"We":[162,180,216,243],"show":[163,217,321],"groups":[165],"cancellations":[170],"transform":[174],"matrices":[175],"error.":[179,215,298],"find":[181],"empirically":[182],"error":[185,256],"different":[187],"values":[188,208],"forms":[191],"rough":[193],"curve":[194],"across":[195],"numbers.":[200],"It":[201],"therefore":[203],"possible":[204],"localize":[206],"lead":[212,294],"lower":[214,234,297],"it":[219,309],"not":[221],"necessary":[222],"choose":[224],"or":[226],"evaluation":[230],"points,":[231],"errors":[235],"be":[237],"achieved":[238],"with":[239],"non-obvious":[240],"study":[244],"sizes":[248],"achieve":[253,326],"reduction":[254,327],"ranging":[257,330],"from":[258,331],"2%":[259],"around":[261],"59%":[262],"1D":[265],"2D":[267],"convolution,":[268],"when":[269,282],"compared":[270],"state":[272],"art.":[275],"Furthermore,":[276],"identify":[278],"patterns":[279],"cases":[281],"select":[284],"subset":[286],"our":[288,323],"proposed":[289,324],"will":[292],"always":[293],"Finally,":[299],"implement":[301],"complete":[303],"layer":[306],"use":[308],"run":[311],"state-of-the-art":[312],"deep":[313],"on":[317],"real":[318],"datasets":[319],"error,":[329],"22%":[332],"63%,":[334],"while":[335],"also":[336],"showing":[337],"how":[338],"an":[339],"increased":[340],"output":[342],"size":[343],"result":[345],"execution":[347],"speed-up":[348],"some":[350],"cases.":[351]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
