{"id":"https://openalex.org/W4297984537","doi":"https://doi.org/10.1109/sbcci55532.2022.9893234","title":"Comparative Analysis of Hardware Implementations of a Convolutional Neural Network","display_name":"Comparative Analysis of Hardware Implementations of a Convolutional Neural Network","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W4297984537","doi":"https://doi.org/10.1109/sbcci55532.2022.9893234"},"language":"en","primary_location":{"id":"doi:10.1109/sbcci55532.2022.9893234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sbcci55532.2022.9893234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 35th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)","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/A5037805681","display_name":"Gabriel H. Eisenkraemer","orcid":"https://orcid.org/0000-0003-2742-210X"},"institutions":[{"id":"https://openalex.org/I33501960","display_name":"Universidade Federal de Santa Maria","ror":"https://ror.org/01b78mz79","country_code":"BR","type":"education","lineage":["https://openalex.org/I33501960"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Gabriel Henrique Eisenkraemer","raw_affiliation_strings":["Federal University of Santa Maria,Santa Maria,Brazil","Federal University of Santa Maria, Santa Maria, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Santa Maria,Santa Maria,Brazil","institution_ids":["https://openalex.org/I33501960"]},{"raw_affiliation_string":"Federal University of Santa Maria, Santa Maria, Brazil","institution_ids":["https://openalex.org/I33501960"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101737160","display_name":"Leonardo Londero de Oliveira","orcid":"https://orcid.org/0000-0002-4489-9949"},"institutions":[{"id":"https://openalex.org/I33501960","display_name":"Universidade Federal de Santa Maria","ror":"https://ror.org/01b78mz79","country_code":"BR","type":"education","lineage":["https://openalex.org/I33501960"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leonardo Londero de Oliveira","raw_affiliation_strings":["Federal University of Santa Maria,Santa Maria,Brazil","Federal University of Santa Maria, Santa Maria, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Santa Maria,Santa Maria,Brazil","institution_ids":["https://openalex.org/I33501960"]},{"raw_affiliation_string":"Federal University of Santa Maria, Santa Maria, Brazil","institution_ids":["https://openalex.org/I33501960"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072419981","display_name":"Everton Alceu Carara","orcid":"https://orcid.org/0000-0003-0276-8462"},"institutions":[{"id":"https://openalex.org/I33501960","display_name":"Universidade Federal de Santa Maria","ror":"https://ror.org/01b78mz79","country_code":"BR","type":"education","lineage":["https://openalex.org/I33501960"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Everton Alceu Carara","raw_affiliation_strings":["Federal University of Santa Maria,Santa Maria,Brazil","Federal University of Santa Maria, Santa Maria, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Santa Maria,Santa Maria,Brazil","institution_ids":["https://openalex.org/I33501960"]},{"raw_affiliation_string":"Federal University of Santa Maria, Santa Maria, Brazil","institution_ids":["https://openalex.org/I33501960"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037805681"],"corresponding_institution_ids":["https://openalex.org/I33501960"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09014331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1706 2393","issue":null,"first_page":"1","last_page":"6"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9979000091552734,"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/T10320","display_name":"Neural Networks and Applications","score":0.9976999759674072,"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":0.8319672346115112},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.6845042109489441},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.660124659538269},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6501492857933044},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6355074644088745},{"id":"https://openalex.org/keywords/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.5707977414131165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5674546957015991},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.47420790791511536},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4531458020210266},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.44504570960998535},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.43086937069892883},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4145847260951996},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.41354283690452576},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.41196173429489136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27543699741363525},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19690054655075073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8319672346115112},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.6845042109489441},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.660124659538269},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6501492857933044},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6355074644088745},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.5707977414131165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5674546957015991},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.47420790791511536},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4531458020210266},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.44504570960998535},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.43086937069892883},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4145847260951996},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.41354283690452576},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.41196173429489136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27543699741363525},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19690054655075073},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sbcci55532.2022.9893234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sbcci55532.2022.9893234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 35th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)","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":15,"referenced_works":["https://openalex.org/W1999085092","https://openalex.org/W2145339207","https://openalex.org/W2194775991","https://openalex.org/W2289254158","https://openalex.org/W2612864759","https://openalex.org/W2622872848","https://openalex.org/W2981820936","https://openalex.org/W2982316857","https://openalex.org/W3038013679","https://openalex.org/W3174389152","https://openalex.org/W4206831315","https://openalex.org/W4295312788","https://openalex.org/W6669893450","https://openalex.org/W6766978945","https://openalex.org/W6769022465"],"related_works":["https://openalex.org/W2928062709","https://openalex.org/W4289729660","https://openalex.org/W2887023857","https://openalex.org/W2950000202","https://openalex.org/W4321472478","https://openalex.org/W3204400881","https://openalex.org/W3214410901","https://openalex.org/W3204296682","https://openalex.org/W3183118997","https://openalex.org/W2917767146"],"abstract_inverted_index":{"Artificial":[0],"Neural":[1],"Networks":[2],"(ANNs)":[3],"have":[4],"become":[5],"the":[6,40,63,97,104,171,178,185,191,195,198,204],"most":[7],"popular":[8],"machine":[9],"learning":[10],"technique":[11],"for":[12,158],"data":[13],"processing,":[14],"performing":[15],"central":[16],"functions":[17],"in":[18,34,58,74,117,128],"a":[19,36,72,112,119,145],"wide":[20],"variety":[21],"of":[22,39,50,67,162,184,190],"applications.":[23],"In":[24,87,109],"many":[25],"cases,":[26],"these":[27,68,88],"models":[28,69],"are":[29,83,93,155],"used":[30],"within":[31],"constrained":[32],"scenarios,":[33,89],"which":[35,118],"local":[37],"execution":[38],"algorithm":[41],"is":[42,70,115,125],"necessary":[43],"to":[44,85,95,102,138],"avoid":[45],"latency":[46],"and":[47,80,99,151,188],"safety":[48],"issues":[49],"remote":[51],"computing":[52],"(e.g,":[53],"autonomous":[54],"vehicles,":[55],"edge":[56],"devices":[57],"IoT":[59],"networks).":[60],"Even":[61],"so,":[62],"known":[64],"computational":[65],"complexity":[66],"still":[71],"challenge":[73],"such":[75],"contexts,":[76],"as":[77],"implementation":[78,106,153],"costs":[79],"performance":[81,200],"requirements":[82],"difficult":[84],"balance.":[86],"pa-rameter":[90],"quantization":[91,147,173],"techniques":[92],"essential":[94],"simplifying":[96],"operations":[98],"memory":[100,192],"footprint":[101],"make":[103],"hardware":[105,129],"more":[107],"viable.":[108],"this":[110],"paper,":[111],"case":[113],"study":[114],"devised":[116],"convolutional":[120],"neural":[121],"network":[122],"(CNN)":[123],"architecture":[124],"fully":[126],"implemented":[127,179],"with":[130,144],"three":[131],"different":[132],"optimization":[133],"strategies,":[134],"having":[135],"parameters":[136],"mapped":[137],"low":[139],"bit-width":[140],"fixed":[141],"point":[142],"integers":[143],"power-of-two":[146],"scheme.":[148],"Both":[149],"ASIC":[150],"FPGA":[152],"flows":[154],"followed,":[156],"allowing":[157],"an":[159],"in-depth":[160],"analysis":[161],"each":[163],"circuit":[164],"version.":[165],"The":[166],"obtained":[167],"results":[168],"show":[169],"that":[170],"adopted":[172],"process":[174],"enables":[175],"optimizations":[176],"on":[177],"circuit,":[180],"reducing":[181],"about":[182],"50%":[183],"circuitry":[186],"area":[187],"87.5%":[189],"requirement.":[193],"At":[194],"same":[196,205],"time,":[197],"application":[199],"was":[201],"kept":[202],"at":[203],"level.":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
