{"id":"https://openalex.org/W2976097345","doi":"https://doi.org/10.1145/3313231.3352378","title":"Flow mapping and data distribution on mesh-based deep learning accelerator","display_name":"Flow mapping and data distribution on mesh-based deep learning accelerator","publication_year":2019,"publication_date":"2019-09-26","ids":{"openalex":"https://openalex.org/W2976097345","doi":"https://doi.org/10.1145/3313231.3352378","mag":"2976097345"},"language":"en","primary_location":{"id":"doi:10.1145/3313231.3352378","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313231.3352378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip","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/A5075524497","display_name":"Seyedeh Yasaman Hosseini Mirmahaleh","orcid":"https://orcid.org/0000-0002-8147-5221"},"institutions":[{"id":"https://openalex.org/I110525433","display_name":"Islamic Azad University, Tehran","ror":"https://ror.org/01kzn7k21","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Seyedeh Yasaman Hosseini Mirmahaleh","raw_affiliation_strings":["Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I110525433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044495764","display_name":"Midia Reshadi","orcid":"https://orcid.org/0000-0001-7628-2401"},"institutions":[{"id":"https://openalex.org/I110525433","display_name":"Islamic Azad University, Tehran","ror":"https://ror.org/01kzn7k21","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Midia Reshadi","raw_affiliation_strings":["Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I110525433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087978342","display_name":"Hesam Shabani","orcid":null},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hesam Shabani","raw_affiliation_strings":["Lehigh University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lehigh University","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028757963","display_name":"Xiaochen Guo","orcid":"https://orcid.org/0000-0001-7704-0412"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaochen Guo","raw_affiliation_strings":["Lehigh University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lehigh University","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012004974","display_name":"Nader Bagherzadeh","orcid":"https://orcid.org/0000-0001-7216-0546"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nader Bagherzadeh","raw_affiliation_strings":["University of California Irvine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Irvine","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2357,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.93233274,"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9993000030517578,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6883217096328735},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4707879424095154},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4329264163970947},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4129326045513153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3780336380004883},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.36200788617134094},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08993396162986755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07791301608085632}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883217096328735},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4707879424095154},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4329264163970947},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4129326045513153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3780336380004883},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.36200788617134094},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08993396162986755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07791301608085632},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3313231.3352378","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313231.3352378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1505822854","https://openalex.org/W1994197834","https://openalex.org/W2048266589","https://openalex.org/W2067523571","https://openalex.org/W2092850952","https://openalex.org/W2112796928","https://openalex.org/W2136189984","https://openalex.org/W2145581844","https://openalex.org/W2277132981","https://openalex.org/W2289252105","https://openalex.org/W2395611524","https://openalex.org/W2473350741","https://openalex.org/W2509453741","https://openalex.org/W2518281301","https://openalex.org/W2752585553","https://openalex.org/W2756031739","https://openalex.org/W2790925711","https://openalex.org/W2798838253","https://openalex.org/W2801037557","https://openalex.org/W2884201348","https://openalex.org/W2901767673","https://openalex.org/W2914925532","https://openalex.org/W2919115771","https://openalex.org/W2953212265","https://openalex.org/W2962836170","https://openalex.org/W2963566954","https://openalex.org/W2963893493","https://openalex.org/W4210984760","https://openalex.org/W6604472716"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W2939353110","https://openalex.org/W2126887587","https://openalex.org/W4312962853","https://openalex.org/W4230611425","https://openalex.org/W2948658236","https://openalex.org/W2941846814","https://openalex.org/W3118091236"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1,50],"networks":[2,51,71],"have":[3,32,76],"been":[4,33,77],"proposed":[5,78],"as":[6],"an":[7],"approach":[8],"for":[9],"classifying":[10],"data":[11,20,88],"corresponding":[12],"to":[13,25,79],"labeled":[14],"and":[15,39,58,62,72,84,114],"unlabeled":[16],"datasets.":[17],"The":[18,43],"fast-growing":[19],"empowers":[21],"deep":[22,48],"learning":[23],"algorithms":[24,38],"achieve":[26],"higher":[27],"accuracy.":[28],"Numerous":[29],"trained":[30,116],"models":[31],"proposed,":[34],"which":[35,130],"involve":[36],"complex":[37],"increasing":[40],"network":[41],"depth.":[42],"main":[44],"challenges":[45],"of":[46,68,87],"implementing":[47],"convolutional":[49],"are":[52],"high":[53,56],"energy":[54,85,132],"consumption,":[55],"on-chip":[57,69],"off-chip":[59],"bandwidth":[60],"requirements,":[61],"large":[63],"memory":[64,81,107],"footprint.":[65],"Different":[66],"types":[67],"communication":[70],"traffic":[73,95],"distribution":[74,96],"methods":[75],"reduce":[80],"access":[82,108],"latency":[83],"consumption":[86,133],"movement.":[89],"This":[90],"paper":[91],"proposes":[92],"a":[93,99,121],"new":[94],"mechanism":[97,109],"on":[98,127],"mesh":[100],"topology":[101],"using":[102],"distributer":[103],"nodes":[104],"by":[105,134],"considering":[106],"in":[110],"the":[111],"AlexNet,":[112],"VggNet,":[113],"GoogleNet":[115],"models.":[117],"We":[118],"also":[119],"propose":[120],"flow":[122],"mapping":[123],"method":[124],"(FMM)":[125],"based":[126],"dataflow":[128],"stationary":[129],"reduces":[131],"8%.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}