{"id":"https://openalex.org/W3134165833","doi":"https://doi.org/10.1109/icce-berlin50680.2020.9352153","title":"Automated Hardware and Neural Network Architecture co-design of FPGA accelerators using multi-objective Neural Architecture Search","display_name":"Automated Hardware and Neural Network Architecture co-design of FPGA accelerators using multi-objective Neural Architecture Search","publication_year":2020,"publication_date":"2020-11-09","ids":{"openalex":"https://openalex.org/W3134165833","doi":"https://doi.org/10.1109/icce-berlin50680.2020.9352153","mag":"3134165833"},"language":"en","primary_location":{"id":"doi:10.1109/icce-berlin50680.2020.9352153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin50680.2020.9352153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055662480","display_name":"Philip Colangelo","orcid":null},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip Colangelo","raw_affiliation_strings":["Intel PSG,San Jose,USA","Intel PSG, San Jose, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intel PSG,San Jose,USA","institution_ids":["https://openalex.org/I1343180700"]},{"raw_affiliation_string":"Intel PSG, San Jose, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029287518","display_name":"Oren Segal","orcid":null},"institutions":[{"id":"https://openalex.org/I139290212","display_name":"Hofstra University","ror":"https://ror.org/03pm18j10","country_code":"US","type":"education","lineage":["https://openalex.org/I139290212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oren Segal","raw_affiliation_strings":["Hofstra University,Hempstead,USA","Hofstra University, Hempstead, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hofstra University,Hempstead,USA","institution_ids":["https://openalex.org/I139290212"]},{"raw_affiliation_string":"Hofstra University, Hempstead, USA","institution_ids":["https://openalex.org/I139290212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085173181","display_name":"Alex Speicher","orcid":null},"institutions":[{"id":"https://openalex.org/I139290212","display_name":"Hofstra University","ror":"https://ror.org/03pm18j10","country_code":"US","type":"education","lineage":["https://openalex.org/I139290212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Speicher","raw_affiliation_strings":["Hofstra University,Hempstead,USA","Hofstra University, Hempstead, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hofstra University,Hempstead,USA","institution_ids":["https://openalex.org/I139290212"]},{"raw_affiliation_string":"Hofstra University, Hempstead, USA","institution_ids":["https://openalex.org/I139290212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062664944","display_name":"Martin Margala","orcid":"https://orcid.org/0000-0002-0034-0369"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Margala","raw_affiliation_strings":["University of Massachusetts Lowell,Lowell,USA","University of Massachusetts Lowell, Lowell, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Lowell,Lowell,USA","institution_ids":["https://openalex.org/I133738476"]},{"raw_affiliation_string":"University of Massachusetts Lowell, Lowell, USA","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.2050471,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9980000257492065,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980000257492065,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.996999979019165,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.815068244934082},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7028820514678955},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6458025574684143},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6305254101753235},{"id":"https://openalex.org/keywords/design-space-exploration","display_name":"Design space exploration","score":0.6103162169456482},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.584115743637085},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5783249139785767},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5239120721817017},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.5080909729003906},{"id":"https://openalex.org/keywords/design-flow","display_name":"Design flow","score":0.49921202659606934},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4761008024215698},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4568342864513397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45455941557884216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3797720968723297},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3782890737056732},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.32747042179107666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.815068244934082},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7028820514678955},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6458025574684143},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6305254101753235},{"id":"https://openalex.org/C2776221188","wikidata":"https://www.wikidata.org/wiki/Q21072556","display_name":"Design space exploration","level":2,"score":0.6103162169456482},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.584115743637085},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5783249139785767},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5239120721817017},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.5080909729003906},{"id":"https://openalex.org/C37135326","wikidata":"https://www.wikidata.org/wiki/Q931942","display_name":"Design flow","level":2,"score":0.49921202659606934},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4761008024215698},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4568342864513397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45455941557884216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3797720968723297},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3782890737056732},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.32747042179107666},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-berlin50680.2020.9352153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin50680.2020.9352153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1568834902","https://openalex.org/W1581066146","https://openalex.org/W1680392829","https://openalex.org/W2111935653","https://openalex.org/W2132862423","https://openalex.org/W2419597278","https://openalex.org/W2594529350","https://openalex.org/W2750384547","https://openalex.org/W2794670651","https://openalex.org/W2801748224","https://openalex.org/W2885311373","https://openalex.org/W2901839763","https://openalex.org/W2931743911","https://openalex.org/W2949264490","https://openalex.org/W2963568120","https://openalex.org/W2963778169","https://openalex.org/W2964081807","https://openalex.org/W2964212578","https://openalex.org/W2965658867","https://openalex.org/W2990728381","https://openalex.org/W4289276774","https://openalex.org/W6633992998","https://openalex.org/W6634833660","https://openalex.org/W6637386731","https://openalex.org/W6717177883","https://openalex.org/W6734593296","https://openalex.org/W6743688258","https://openalex.org/W6745614327","https://openalex.org/W6747806236","https://openalex.org/W6753278433","https://openalex.org/W6756580189"],"related_works":["https://openalex.org/W2037960874","https://openalex.org/W2091330445","https://openalex.org/W4313341326","https://openalex.org/W1973069902","https://openalex.org/W2029518208","https://openalex.org/W2269990635","https://openalex.org/W1968547622","https://openalex.org/W4234221021","https://openalex.org/W1567432572","https://openalex.org/W4365793791"],"abstract_inverted_index":{"State-of-the-art":[0],"Neural":[1,32],"Network":[2],"Architectures":[3],"(NNAs)":[4],"are":[5,39,144],"challenging":[6],"to":[7,23,42,52,210],"design":[8,161,184],"and":[9,28,50,57,74,100,115,120,143,153,162,169,190,208],"implement":[10],"efficiently":[11],"in":[12,26,64,87],"hardware.":[13],"In":[14,95],"the":[15,46,61,65,78,85,125,135,176,183,195],"past":[16],"couple":[17],"of":[18,30,45,60,67,84,137,178,197],"years,":[19],"this":[20,96],"has":[21,69],"led":[22],"an":[24],"explosion":[25],"research":[27,63],"development":[29],"automatic":[31],"Architecture":[33],"Search":[34],"(NAS)":[35],"tools.":[36],"AutoML":[37],"tools":[38],"now":[40],"used":[41],"achieve":[43,166],"state":[44],"art":[47],"NNA":[48],"designs":[49],"attempt":[51],"optimize":[53],"for":[54,175,186],"hardware":[55,121,193],"usage":[56],"design.":[58],"Much":[59],"recent":[62],"auto-design":[66],"NNAs":[68,119],"focused":[70],"on":[71,127],"convolution":[72],"networks":[73,189],"image":[75],"recognition,":[76],"ignoring":[77],"fact":[79],"that":[80,108],"a":[81,102],"significant":[82],"part":[83],"workload":[86],"data":[88],"centers":[89],"is":[90],"general-purpose":[91],"deep":[92],"neural":[93],"networks.":[94],"work,":[97],"we":[98,133,164],"develop":[99],"test":[101,124],"general":[103,152],"multilayer":[104],"perceptron":[105],"(MLP)":[106],"flow":[107,126],"can":[109,165],"take":[110],"arbitrary":[111],"datasets":[112],"as":[113],"input":[114],"automatically":[116],"produce":[117],"optimized":[118],"designs.":[122],"We":[123,150],"six":[128],"benchmarks.":[129],"Our":[130],"results":[131,142],"show":[132,163],"exceed":[134],"performance":[136],"currently":[138],"published":[139],"MLP":[140],"accuracy":[141,201],"competitive":[145],"with":[146,157],"non-MLP":[147],"based":[148],"results.":[149],"compare":[151],"common":[154],"GPU":[155],"architectures":[156],"our":[158],"scalable":[159],"FPGA":[160],"higher":[167,170],"efficiency":[168],"throughput":[171],"(outputs":[172],"per":[173],"second)":[174],"majority":[177],"datasets.":[179],"Further":[180],"insights":[181],"into":[182],"space":[185],"both":[187],"accurate":[188],"high":[191],"performing":[192],"shows":[194],"power":[196],"co-design":[198],"by":[199],"correlating":[200],"versus":[202,206],"throughput,":[203],"network":[204],"size":[205],"accuracy,":[207],"scaling":[209],"high-performance":[211],"devices.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
