{"id":"https://openalex.org/W2521912036","doi":"https://doi.org/10.1109/lca.2017.2656880","title":"Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks","display_name":"Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks","publication_year":2017,"publication_date":"2017-01-23","ids":{"openalex":"https://openalex.org/W2521912036","doi":"https://doi.org/10.1109/lca.2017.2656880","mag":"2521912036"},"language":"en","primary_location":{"id":"doi:10.1109/lca.2017.2656880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lca.2017.2656880","pdf_url":null,"source":{"id":"https://openalex.org/S17643076","display_name":"IEEE Computer Architecture Letters","issn_l":"1556-6056","issn":["1556-6056","1556-6064","2473-2575"],"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 Computer Architecture Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1609.05132","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027621194","display_name":"J. W. Garland","orcid":"https://orcid.org/0000-0002-8688-9407"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"James Garland","raw_affiliation_strings":["Trinity College Dublin, Dublin 2, Ireland"],"affiliations":[{"raw_affiliation_string":"Trinity College Dublin, Dublin 2, Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003800161","display_name":"David Gregg","orcid":"https://orcid.org/0000-0003-3782-4612"},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"David Gregg","raw_affiliation_strings":["Trinity College Dublin, Dublin 2, Ireland"],"affiliations":[{"raw_affiliation_string":"Trinity College Dublin, Dublin 2, Ireland","institution_ids":["https://openalex.org/I205274468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027621194"],"corresponding_institution_ids":["https://openalex.org/I205274468"],"apc_list":null,"apc_paid":null,"fwci":0.9267,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.83407151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"16","issue":"2","first_page":"132","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9995999932289124,"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/T12676","display_name":"Machine Learning and ELM","score":0.9973000288009644,"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":0.8293046951293945},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7280239462852478},{"id":"https://openalex.org/keywords/adder","display_name":"Adder","score":0.7102786898612976},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5366525053977966},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.45055460929870605},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3735053241252899},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3454068899154663},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25766658782958984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8293046951293945},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7280239462852478},{"id":"https://openalex.org/C164620267","wikidata":"https://www.wikidata.org/wiki/Q376953","display_name":"Adder","level":3,"score":0.7102786898612976},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5366525053977966},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.45055460929870605},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3735053241252899},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3454068899154663},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25766658782958984},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lca.2017.2656880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lca.2017.2656880","pdf_url":null,"source":{"id":"https://openalex.org/S17643076","display_name":"IEEE Computer Architecture Letters","issn_l":"1556-6056","issn":["1556-6056","1556-6064","2473-2575"],"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 Computer Architecture Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1609.05132","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1609.05132","pdf_url":"https://arxiv.org/pdf/1609.05132","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1609.05132","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1609.05132","pdf_url":"https://arxiv.org/pdf/1609.05132","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7863452979","display_name":null,"funder_award_id":"12/IA/1381","funder_id":"https://openalex.org/F4320320847","funder_display_name":"Science Foundation Ireland"}],"funders":[{"id":"https://openalex.org/F4320320847","display_name":"Science Foundation Ireland","ror":"https://ror.org/0271asj38"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1841592590","https://openalex.org/W2015861736","https://openalex.org/W2094756095","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2152839228","https://openalex.org/W2154579312","https://openalex.org/W2170835539","https://openalex.org/W2285660444","https://openalex.org/W2409361203","https://openalex.org/W2442974303","https://openalex.org/W2963374099","https://openalex.org/W2964299589","https://openalex.org/W6638783484"],"related_works":["https://openalex.org/W4390550886","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W4390846322"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"are":[4,87],"one":[5],"of":[6,25,35,57,81,98,129],"the":[7,33,78,92,99,121,127,141,154,169],"most":[8],"successful":[9],"deep":[10],"machine":[11],"learning":[12],"technologies":[13],"for":[14,44,50,168],"processing":[15,26],"image,":[16],"voice":[17],"and":[18,28,39,68,91,132,162,181],"video":[19],"data.":[20],"CNNs":[21,51],"require":[22],"large":[23,55],"amounts":[24],"capacity":[27],"memory,":[29],"which":[30,52],"can":[31],"exceed":[32],"resources":[34],"low":[36],"power":[37],"mobile":[38],"embedded":[40],"systems.":[41],"Several":[42],"designs":[43],"hardware":[45,151],"accelerators":[46,73],"have":[47],"been":[48],"proposed":[49],"typically":[53],"contain":[54],"numbers":[56],"Multiply":[58],"Accumulate":[59],"(MAC)":[60],"units.":[61],"One":[62],"approach":[63,174],"to":[64,157],"reducing":[65],"data":[66],"sizes":[67],"memory":[69],"traffic":[70],"in":[71,83,89,115,135,144,153,176],"CNN":[72,86],"is":[74,95],"\u201cweight":[75],"sharing\u201d,":[76],"where":[77],"full":[79],"range":[80],"values":[82],"a":[84,108,136,145],"trained":[85],"put":[88],"bins":[90],"bin":[93],"index":[94],"stored":[96],"instead":[97,125],"original":[100],"weight":[101,131],"value.":[102],"In":[103],"this":[104],"paper":[105],"we":[106,124],"propose":[107],"novel":[109],"MAC":[110,122,155],"circuit":[111,156],"that":[112,167],"exploits":[113],"binning":[114],"weight-sharing":[116],"CNNs.":[117],"Rather":[118],"than":[119],"computing":[120],"directly":[123],"count":[126],"frequency":[128],"each":[130],"place":[133],"it":[134],"bin.":[137],"We":[138],"then":[139],"compute":[140],"accumulated":[142],"value":[143],"subsequent":[146],"multiply":[147],"phase.":[148],"This":[149],"allows":[150],"multipliers":[152],"be":[158],"replaced":[159],"with":[160],"adders":[161],"selection":[163],"logic.":[164],"Experiments":[165],"show":[166],"same":[170],"clock":[171],"speed":[172],"our":[173],"results":[175],"fewer":[177],"gates,":[178],"smaller":[179],"logic,":[180],"reduced":[182],"power.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
