{"id":"https://openalex.org/W4408281849","doi":"https://doi.org/10.1109/iecon55916.2024.10905517","title":"A hardware acceleration Framework of Reconfigurable Edge Modules for Convolutional Neural Networks","display_name":"A hardware acceleration Framework of Reconfigurable Edge Modules for Convolutional Neural Networks","publication_year":2024,"publication_date":"2024-11-03","ids":{"openalex":"https://openalex.org/W4408281849","doi":"https://doi.org/10.1109/iecon55916.2024.10905517"},"language":"en","primary_location":{"id":"doi:10.1109/iecon55916.2024.10905517","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon55916.2024.10905517","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society","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/A5043983485","display_name":"Yiming Qiao","orcid":"https://orcid.org/0000-0001-8023-0276"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiming Qiao","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017044026","display_name":"Zidi Jia","orcid":"https://orcid.org/0000-0002-3746-7742"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zidi Jia","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043903440","display_name":"Shixiang Li","orcid":"https://orcid.org/0009-0007-9899-6916"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shixiang Li","raw_affiliation_strings":["Beihang University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028426177","display_name":"Lei Ren","orcid":"https://orcid.org/0000-0001-6346-6930"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Ren","raw_affiliation_strings":["Beihang University,Zhongguancun Laboratory State Key Laboratory of Intelligent Manufacturing System Technology,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Zhongguancun Laboratory State Key Laboratory of Intelligent Manufacturing System Technology,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043983485"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25943841,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.8055999875068665,"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/T10320","display_name":"Neural Networks and Applications","score":0.8055999875068665,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.7802000045776367,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.7010999917984009,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7579727172851562},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7130629420280457},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.7072107791900635},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.6413030624389648},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5991116166114807},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.534408450126648},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.4792649447917938},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3896363377571106},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3860138952732086},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26992565393447876}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7579727172851562},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7130629420280457},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.7072107791900635},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.6413030624389648},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5991116166114807},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.534408450126648},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.4792649447917938},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3896363377571106},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3860138952732086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26992565393447876},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon55916.2024.10905517","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon55916.2024.10905517","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2585720638","https://openalex.org/W2727238169","https://openalex.org/W3036920006","https://openalex.org/W3041192231","https://openalex.org/W3081693237","https://openalex.org/W3082966435","https://openalex.org/W3126600466","https://openalex.org/W3200780112","https://openalex.org/W4225975032","https://openalex.org/W4312456926"],"related_works":["https://openalex.org/W2111241003","https://openalex.org/W2355315220","https://openalex.org/W4200391368","https://openalex.org/W1967938402","https://openalex.org/W2386041993","https://openalex.org/W1608572506","https://openalex.org/W2146872326","https://openalex.org/W2518118925","https://openalex.org/W3158825072","https://openalex.org/W3159273459"],"abstract_inverted_index":{"Edge":[0],"computing":[1,14,97],"exploits":[2],"node":[3],"devices":[4,43,62,108],"situated":[5],"in":[6,29,137],"close":[7],"proximity":[8],"to":[9,11,17,79,94,139],"terminals":[10],"deliver":[12],"distributed":[13],"services":[15],"directly":[16],"users,":[18],"with":[19],"FPGAs":[20],"serving":[21],"as":[22,121],"the":[23,27,124,127],"predominant":[24],"platform.":[25],"With":[26],"amplification":[28],"volume":[30],"and":[31,117,134,141],"intricacy":[32],"of":[33],"deep":[34],"learning":[35],"models,":[36],"effectively":[37],"deploying":[38],"these":[39],"models":[40,76],"on":[41,106],"FPGA":[42,107,128],"introduces":[44,53],"substantial":[45],"challenges.":[46],"To":[47],"counter":[48],"this":[49,51],"predicament,":[50],"paper":[52],"an":[54],"FPGA-based":[55],"hardware":[56,89],"acceleration":[57,90],"framework":[58],"for":[59,64,72,109],"reconfigurable":[60],"edge":[61],"tailored":[63],"Convolutional":[65],"Neural":[66],"Networks":[67],"(CNNs).":[68],"Initially,":[69],"a":[70,86],"method":[71],"dynamically":[73],"quantizing":[74],"network":[75],"is":[77,92,112],"devised":[78],"markedly":[80],"curtail":[81],"model":[82,104],"memory":[83],"consumption.":[84],"Subsequently,":[85],"meticulously":[87],"engineered":[88],"unit":[91,130],"formulated":[93],"attain":[95],"augmented":[96],"parallelism":[98],"via":[99],"meticulous":[100],"temporal":[101],"redesign.":[102],"Finally,":[103],"deployment":[105],"inference":[110,129],"verification":[111],"showcased":[113],"utilizing":[114],"fully":[115],"connected":[116],"convolutional":[118],"neural":[119],"networks":[120],"exemplars.":[122],"On":[123],"MNIST":[125],"dataset,":[126],"attains":[131],"remarkable":[132],"accuracy":[133],"computational":[135],"efficiency":[136],"comparison":[138],"CPUs":[140],"GPUs":[142]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
