{"id":"https://openalex.org/W2918274546","doi":"https://doi.org/10.1109/isqed.2019.8697647","title":"Towards Collaborative Intelligence Friendly Architectures for Deep Learning","display_name":"Towards Collaborative Intelligence Friendly Architectures for Deep Learning","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2918274546","doi":"https://doi.org/10.1109/isqed.2019.8697647","mag":"2918274546"},"language":"en","primary_location":{"id":"doi:10.1109/isqed.2019.8697647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed.2019.8697647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"20th International Symposium on Quality Electronic Design (ISQED)","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/A5040410841","display_name":"Amir Erfan Eshratifar","orcid":"https://orcid.org/0000-0002-1339-7671"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amir Erfan Eshratifar","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081336995","display_name":"Amirhossein Esmaili","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amirhossein Esmaili","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044650311","display_name":"Massoud Pedram","orcid":"https://orcid.org/0000-0002-2677-7307"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Massoud Pedram","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":2.4705,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.92866527,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"14","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9977999925613403,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7857354879379272},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.7370463609695435},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.6134726405143738},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6054421067237854},{"id":"https://openalex.org/keywords/graphics-processing-unit","display_name":"Graphics processing unit","score":0.4845668375492096},{"id":"https://openalex.org/keywords/mobile-computing","display_name":"Mobile computing","score":0.4391443133354187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39121127128601074},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.38461393117904663},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.26033803820610046},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.23711660504341125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7857354879379272},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7370463609695435},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.6134726405143738},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6054421067237854},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.4845668375492096},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.4391443133354187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39121127128601074},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.38461393117904663},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.26033803820610046},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.23711660504341125}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isqed.2019.8697647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed.2019.8697647","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"20th International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1667652561","https://openalex.org/W2108598243","https://openalex.org/W2129636357","https://openalex.org/W2153579005","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W2183182206","https://openalex.org/W2194775991","https://openalex.org/W2605258629","https://openalex.org/W2806506455","https://openalex.org/W2889525847","https://openalex.org/W2891178669","https://openalex.org/W2962790536","https://openalex.org/W2963341924","https://openalex.org/W2963770864","https://openalex.org/W2964299589","https://openalex.org/W2981114133","https://openalex.org/W4235435541","https://openalex.org/W4294170691","https://openalex.org/W4302296459","https://openalex.org/W6631943919","https://openalex.org/W6637151318","https://openalex.org/W6682691769","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6717697761"],"related_works":["https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2048100608","https://openalex.org/W2090296580","https://openalex.org/W1576249345","https://openalex.org/W4243905374","https://openalex.org/W2785815065","https://openalex.org/W1796074903","https://openalex.org/W4245955065"],"abstract_inverted_index":{"Modern":[0],"mobile":[1,33,37,84,113,125,143,293,336],"devices":[2,38],"are":[3,29,39,60,145,269,288],"equipped":[4],"with":[5,53,258],"highperformance":[6],"hardware":[7],"resources":[8],"such":[9],"as":[10,26,135,225,281],"graphics":[11],"processing":[12],"units":[13,276],"(GPUs),":[14],"making":[15],"the":[16,65,80,83,95,103,121,124,128,142,148,152,157,160,163,170,190,193,201,226,234,238,244,250,255,259,263,266,272,292,300,309,341,349],"end-side":[17],"intelligent":[18,58],"services":[19],"more":[20],"feasible.":[21],"Even":[22],"recently,":[23],"specialized":[24],"silicons":[25],"neural":[27,110],"engines":[28],"being":[30],"used":[31],"for":[32,56,188,328,335,346],"devices.":[34],"However,":[35],"most":[36],"still":[40],"not":[41],"capable":[42],"of":[43,73,108,151,156,162,192,216,233,243,265,308],"performing":[44],"real-time":[45],"inference":[46,267,286],"using":[47],"very":[48],"deep":[49,54,109,218,311],"models.":[50],"Computations":[51],"associated":[52,257],"models":[55],"today's":[57],"applications":[59,114],"typically":[61],"performed":[62],"solely":[63],"on":[64,94,141,253,324],"cloud.":[66,129,171],"This":[67,220],"cloud-only":[68,344],"approach":[69,345],"requires":[70],"significant":[71],"amounts":[72],"raw":[74,153],"data":[75,155,164,195],"to":[76,79,134,147,166,169,197,200,203,224,249,291,340],"be":[77,116,167,198],"uploaded":[78],"cloud":[81,96,149,202,251],"over":[82],"wireless":[85,321],"network":[86,268,297],"and":[87,91,105,127,237,262,274,331],"imposes":[88],"considerable":[89],"computational":[90],"communication":[92],"load":[93],"server.":[97],"Recent":[98],"studies":[99],"have":[100],"shown":[101],"that":[102],"latency":[104,330],"energy":[106,337],"consumption":[107,338],"networks":[111],"in":[112],"can":[115],"notably":[117],"reduced":[118],"by":[119,183],"splitting":[120],"workload":[122],"between":[123],"device":[126,144],"In":[130,172],"this":[131,173,208],"approach,":[132],"referred":[133,223],"collaborative":[136,179],"intelligence,":[137],"intermediate":[138],"features":[139],"computed":[140],"offloaded":[146,199,248],"instead":[150],"input":[154],"network,":[158],"reducing":[159,189],"size":[161,191],"needed":[165,196],"sent":[168,289],"paper,":[174],"we":[175],"design":[176],"a":[177,185,204,213,217,278,305],"new":[178,296],"intelligence":[180],"friendly":[181],"architecture":[182],"introducing":[184],"unit":[186,209,221,231,236,246,261,303],"responsible":[187],"feature":[194],"greater":[205],"extent,":[206],"where":[207],"is":[210,222,247,313,352],"placed":[211],"after":[212,304],"selected":[214,306],"layer":[215,280,307],"model.":[219],"butterfly":[227,230,302],"unit.":[228,240],"The":[229,241,285,295],"consists":[232],"reduction":[235,245,273],"restoration":[239,260,275],"outputs":[242],"server":[252],"which":[254],"computations":[256],"rest":[264],"performed.":[270],"Both":[271],"use":[277],"convolutional":[279],"their":[282],"main":[283],"component.":[284],"outcomes":[287],"back":[290],"device.":[294],"architecture,":[298],"including":[299],"introduced":[301],"underlying":[310],"model,":[312],"trained":[314],"end-to-end.":[315],"Our":[316],"proposed":[317],"method,":[318],"across":[319],"different":[320],"networks,":[322],"achieves":[323],"average":[325],"53x":[326],"improvements":[327,334],"end-to-end":[329],"68":[332],"x":[333],"compared":[339],"status":[342],"quo":[343],"ResNet-50,":[347],"while":[348],"accuracy":[350],"loss":[351],"less":[353],"than":[354],"2":[355],"%.":[356]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
