{"id":"https://openalex.org/W3036361209","doi":"https://doi.org/10.23919/date48585.2020.9116429","title":"DC-CNN: Computational Flow Redefinition for Efficient CNN through Structural Decoupling","display_name":"DC-CNN: Computational Flow Redefinition for Efficient CNN through Structural Decoupling","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3036361209","doi":"https://doi.org/10.23919/date48585.2020.9116429","mag":"3036361209"},"language":"en","primary_location":{"id":"doi:10.23919/date48585.2020.9116429","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-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/A5103085687","display_name":"Fuxun Yu","orcid":"https://orcid.org/0000-0002-4880-6658"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fuxun Yu","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018832267","display_name":"Zhuwei Qin","orcid":"https://orcid.org/0000-0002-5465-7740"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuwei Qin","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017918652","display_name":"Di Wang","orcid":"https://orcid.org/0000-0003-1303-4819"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["Microsoft, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108580063","display_name":"Ping Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ping Xu","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767202","display_name":"Chenchen Liu","orcid":"https://orcid.org/0000-0001-7749-0640"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Liu","raw_affiliation_strings":["University of Maryland, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073942971","display_name":"Zhi Tian","orcid":"https://orcid.org/0000-0002-2738-6826"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi Tian","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100441901","display_name":"Xiang Chen","orcid":"https://orcid.org/0000-0001-8259-4815"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Chen","raw_affiliation_strings":["George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5103085687"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.4885,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.6524837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1097","last_page":"1102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9969000220298767,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.8564804792404175},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6985375285148621},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6886984705924988},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.5856791138648987},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5745934247970581},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5002846717834473},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.475763738155365},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.4423443078994751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.359893262386322},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34999603033065796},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23290219902992249}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8564804792404175},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6985375285148621},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6886984705924988},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.5856791138648987},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5745934247970581},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5002846717834473},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.475763738155365},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.4423443078994751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.359893262386322},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34999603033065796},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23290219902992249},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date48585.2020.9116429","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1799366690","https://openalex.org/W1825675169","https://openalex.org/W2094756095","https://openalex.org/W2114766824","https://openalex.org/W2117696986","https://openalex.org/W2119144962","https://openalex.org/W2253993278","https://openalex.org/W2565305208","https://openalex.org/W2584311934","https://openalex.org/W2610018085","https://openalex.org/W2731857860","https://openalex.org/W2799117673","https://openalex.org/W2900423325","https://openalex.org/W2962965870","https://openalex.org/W2963363373","https://openalex.org/W2963749936","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W4249932213","https://openalex.org/W4300485620","https://openalex.org/W6638389677","https://openalex.org/W6638444622","https://openalex.org/W6677103964","https://openalex.org/W6677580257","https://openalex.org/W6691820373","https://openalex.org/W6726275242","https://openalex.org/W6755904014"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W2060986072","https://openalex.org/W1566155057","https://openalex.org/W2052574922","https://openalex.org/W2065045110","https://openalex.org/W64588465","https://openalex.org/W3120641340","https://openalex.org/W2117825986","https://openalex.org/W2110872974"],"abstract_inverted_index":{"Recently":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(CNNs)":[4],"are":[5],"widely":[6],"applied":[7],"into":[8,74,93],"novel":[9,64],"intelligent":[10],"applications":[11],"and":[12,77,95,110,131,148,161],"systems.":[13],"However,":[14],"the":[15,28,79,89,103,141,153],"CNN":[16,65,72,90,104],"computation":[17,24,42,91,105],"performance":[18,47,106],"is":[19],"significantly":[20,101],"hindered":[21],"by":[22,32,143],"its":[23],"flow,":[25],"which":[26,69,99],"computes":[27],"model":[29],"structure":[30],"sequentially":[31],"layers":[33],"with":[34,107],"massive":[35],"convolution":[36],"operations.":[37],"Such":[38],"a":[39,63],"layer-wise":[40],"sequential":[41],"flow":[43,92],"can":[44,100],"cause":[45],"certain":[46],"issues,":[48],"such":[49],"as":[50],"resource":[51],"under-utilization,":[52],"huge":[53],"memory":[54,154],"overhead,":[55],"etc.":[56],"To":[57],"solve":[58],"these":[59],"problems,":[60],"we":[61,87],"propose":[62],"structural":[66],"decoupling":[67],"method,":[68,86],"could":[70,120,139,156],"decouple":[71],"models":[73],"\"critical":[75],"paths\"":[76],"eliminate":[78],"inter-layer":[80],"data":[81],"dependency.":[82],"Based":[83],"on":[84,126,146,150],"this":[85],"redefine":[88],"parallel":[94],"cascade":[96,137],"computing":[97,138],"paradigms,":[98],"enhance":[102],"both":[108],"multi-core":[109,127],"single-core":[111],"CPU":[112],"processors.":[113],"Experiments":[114],"show":[115],"that,":[116],"our":[117],"DC-CNN":[118],"framework":[119],"reduce":[121,140],"24%":[122,145],"to":[123],"33%":[124],"latency":[125,142],"CPUs":[128],"for":[129],"CIFAR":[130],"ImageNet.":[132],"On":[133],"small-capacity":[134],"mobile":[135],"platforms,":[136],"average":[144,159],"ImageNet":[147],"42%":[149],"CIFAR10.":[151],"Meanwhile,":[152],"reduction":[155],"also":[157],"reach":[158],"21%":[160],"64%,":[162],"respectively.":[163]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}