{"id":"https://openalex.org/W4327808157","doi":"https://doi.org/10.1109/lsp.2023.3258863","title":"HAW: Hardware-Aware Point Selection for Efficient Winograd Convolution","display_name":"HAW: Hardware-Aware Point Selection for Efficient Winograd Convolution","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4327808157","doi":"https://doi.org/10.1109/lsp.2023.3258863"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2023.3258863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3258863","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","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/A5057770686","display_name":"Chaoran Li","orcid":"https://orcid.org/0000-0002-5692-4515"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chaoran Li","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015639918","display_name":"Penglong Jiang","orcid":"https://orcid.org/0000-0002-9062-8517"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Penglong Jiang","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055907553","display_name":"Hui Zhou","orcid":"https://orcid.org/0000-0003-3061-1366"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhou","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008290879","display_name":"Xiaofeng Wang","orcid":"https://orcid.org/0000-0002-3512-3751"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofeng Wang","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023323668","display_name":"Xiongbo Zhao","orcid":"https://orcid.org/0000-0001-9216-2020"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongbo Zhao","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5057770686"],"corresponding_institution_ids":["https://openalex.org/I4210166468"],"apc_list":null,"apc_paid":null,"fwci":0.6149,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.6786448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"30","issue":null,"first_page":"269","last_page":"273"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9968000054359436,"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":0.9968000054359436,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.992900013923645,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7363163232803345},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6617226600646973},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6013848185539246},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5561907887458801},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5320951342582703},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49433159828186035},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42053985595703125},{"id":"https://openalex.org/keywords/fixed-point-arithmetic","display_name":"Fixed-point arithmetic","score":0.4167366027832031},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4157114624977112},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.41076943278312683},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.37621012330055237},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.37474244832992554},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.3697481155395508},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.31627482175827026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29960566759109497},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23369303345680237}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7363163232803345},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6617226600646973},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6013848185539246},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5561907887458801},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5320951342582703},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49433159828186035},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42053985595703125},{"id":"https://openalex.org/C163973906","wikidata":"https://www.wikidata.org/wiki/Q649900","display_name":"Fixed-point arithmetic","level":3,"score":0.4167366027832031},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4157114624977112},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.41076943278312683},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.37621012330055237},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.37474244832992554},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.3697481155395508},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31627482175827026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29960566759109497},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23369303345680237},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2023.3258863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3258863","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1005811612","https://openalex.org/W1487564550","https://openalex.org/W1789336918","https://openalex.org/W1983394510","https://openalex.org/W2172654076","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2592875630","https://openalex.org/W2789246071","https://openalex.org/W2809624076","https://openalex.org/W2907172645","https://openalex.org/W2933543903","https://openalex.org/W2940399336","https://openalex.org/W2982446276","https://openalex.org/W2997109118","https://openalex.org/W3011121930","https://openalex.org/W3098382995","https://openalex.org/W3118608800","https://openalex.org/W3136402052","https://openalex.org/W3196072617","https://openalex.org/W3204278478","https://openalex.org/W4243682116","https://openalex.org/W4293102204","https://openalex.org/W4302296459","https://openalex.org/W4312557470","https://openalex.org/W6637151318","https://openalex.org/W6638020065","https://openalex.org/W6684921986","https://openalex.org/W6695314431","https://openalex.org/W6713134421","https://openalex.org/W6729837857","https://openalex.org/W6734433951","https://openalex.org/W6753069482","https://openalex.org/W6757703747","https://openalex.org/W6758002897","https://openalex.org/W6766978945","https://openalex.org/W6773949105","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2017990332","https://openalex.org/W2080337923","https://openalex.org/W1488776355","https://openalex.org/W4244262766","https://openalex.org/W1982528625","https://openalex.org/W3206224488","https://openalex.org/W2288960809","https://openalex.org/W2062935593","https://openalex.org/W1935080020","https://openalex.org/W965527374"],"abstract_inverted_index":{"Winograd's":[0],"minimal":[1],"filtering":[2],"algorithm":[3],"effectively":[4],"reduces":[5],"the":[6,32,35,45,59,65,83,98],"multiplication":[7],"arithmetic":[8],"complexity":[9],"of":[10,34,39],"Convolutional":[11],"Neural":[12],"Networks.":[13],"However,":[14],"Winograd":[15,40,90],"convolutions":[16,41,117],"in":[17,124],"current":[18],"implementations":[19],"are":[20],"limited":[21],"to":[22,48,57,96,107],"small":[23],"feature":[24],"tiles":[25],"for":[26,88],"two":[27],"reasons:the":[28],"numerical":[29,72,109],"error":[30],"and":[31,74,111,127],"overhead":[33],"transformations.":[36],"The":[37],"performance":[38],"is":[42],"determined":[43],"by":[44],"points":[46],"used":[47],"construct":[49],"transformation":[50],"matrices,":[51],"which":[52,92],"raises":[53],"a":[54],"great":[55],"challenge":[56],"find":[58],"optimal":[60],"points:":[61],"it":[62],"requires":[63],"exploring":[64],"vast":[66],"design":[67,103],"space":[68],"trading":[69],"off":[70],"between":[71],"accuracy":[73,110],"hardware":[75],"resource":[76],"consumption.":[77],"In":[78],"this":[79],"letter,":[80],"we":[81],"introduce":[82],"Hardware-Aware":[84],"point":[85,99],"selection":[86,100],"framework":[87],"efficient":[89],"convolution,":[91],"leverages":[93],"reinforcement":[94],"learning":[95],"determine":[97],"policy.":[101],"We":[102],"three":[104],"reward":[105],"functions":[106],"optimize":[108],"circuit":[112,125],"area.":[113],"Experiments":[114],"demonstrate":[115],"thatWinograd":[116],"using":[118],"our":[119],"policies":[120],"outperform":[121],"state-of-the-art":[122],"methods":[123],"area":[126],"accuracy.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}