{"id":"https://openalex.org/W2756437767","doi":"https://doi.org/10.1145/3130218.3130219","title":"3D NoC-Enabled Heterogeneous Manycore Architectures for Accelerating CNN Training","display_name":"3D NoC-Enabled Heterogeneous Manycore Architectures for Accelerating CNN Training","publication_year":2017,"publication_date":"2017-09-20","ids":{"openalex":"https://openalex.org/W2756437767","doi":"https://doi.org/10.1145/3130218.3130219","mag":"2756437767"},"language":"en","primary_location":{"id":"doi:10.1145/3130218.3130219","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3130218.3130219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh IEEE/ACM International Symposium on Networks-on-Chip","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/A5033021422","display_name":"Biresh Kumar Joardar","orcid":"https://orcid.org/0000-0002-7668-2824"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Biresh Kumar Joardar","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, U.S.A","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028111613","display_name":"Wonje Choi","orcid":"https://orcid.org/0000-0001-7894-2696"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wonje Choi","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, U.S.A","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089540762","display_name":"Ryan Kim","orcid":"https://orcid.org/0000-0001-9249-3292"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Gary Kim","raw_affiliation_strings":["ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055445718","display_name":"Janardhan Rao Doppa","orcid":"https://orcid.org/0000-0002-3848-5301"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Janardhan Rao Doppa","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, U.S.A","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078441163","display_name":"Partha Pratim Pande","orcid":"https://orcid.org/0000-0002-5930-8531"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Partha Pratim Pande","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, U.S.A","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065985595","display_name":"Diana Marculescu","orcid":"https://orcid.org/0000-0002-5734-4221"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diana Marculescu","raw_affiliation_strings":["ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036227385","display_name":"Radu M\u0103rculescu","orcid":"https://orcid.org/0000-0003-1826-7646"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Radu Marculescu","raw_affiliation_strings":["ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ECE Department, Carnegie Mellon University, Pittsburgh, PA, U.S.A","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4619,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.83594758,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9997000098228455,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8052202463150024},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6814374327659607},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.528226375579834},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5128898620605469},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.47097161412239075},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.4513390362262726},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.32125455141067505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2667592763900757}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8052202463150024},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6814374327659607},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.528226375579834},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5128898620605469},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.47097161412239075},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.4513390362262726},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32125455141067505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2667592763900757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3130218.3130219","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3130218.3130219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eleventh IEEE/ACM International Symposium on Networks-on-Chip","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1541633348","https://openalex.org/W1941428696","https://openalex.org/W1987293146","https://openalex.org/W2023264348","https://openalex.org/W2048266589","https://openalex.org/W2066985990","https://openalex.org/W2071003187","https://openalex.org/W2074278632","https://openalex.org/W2079248286","https://openalex.org/W2093043622","https://openalex.org/W2104492856","https://openalex.org/W2106404421","https://openalex.org/W2106562406","https://openalex.org/W2112796928","https://openalex.org/W2122636510","https://openalex.org/W2123406209","https://openalex.org/W2130820665","https://openalex.org/W2140855716","https://openalex.org/W2141973190","https://openalex.org/W2147541574","https://openalex.org/W2159218826","https://openalex.org/W2513524819","https://openalex.org/W2531751603","https://openalex.org/W2919115771","https://openalex.org/W2963565222","https://openalex.org/W3118608800","https://openalex.org/W3142845206","https://openalex.org/W4237131533"],"related_works":["https://openalex.org/W3193565141","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W2954307240","https://openalex.org/W4220731687","https://openalex.org/W2524802307","https://openalex.org/W4283718600","https://openalex.org/W2965586238","https://openalex.org/W2932459076","https://openalex.org/W3186240590"],"abstract_inverted_index":{"As":[0],"deep":[1,36],"learning":[2,37],"technology":[3],"is":[4],"increasingly":[5],"employed":[6,113],"in":[7,171],"diverse":[8],"applications":[9],"domains,":[10],"the":[11,46,51,90,108,139],"demand":[12],"for":[13,80,134],"computational":[14],"power":[15],"to":[16,129],"enable":[17],"these":[18,39],"algorithms":[19],"also":[20,99],"increases.":[21],"In":[22,72],"this":[23,73,104],"respect,":[24],"high-performance":[25],"three-dimensional":[26],"(3D)":[27],"heterogeneous":[28,82],"manycore":[29],"systems":[30,40],"present":[31],"a":[32,77,81,131,152],"promising":[33],"direction.":[34],"However,":[35],"on":[38],"pose":[41],"several":[42],"design":[43,78,162],"challenges.":[44],"First,":[45],"network-on-chip":[47],"(NoC)":[48],"must":[49,64],"handle":[50],"traffic":[52,91],"requirements":[53,92],"of":[54,93,110],"both":[55,94],"CPU":[56],"and":[57,96,120],"GPU":[58],"communications.":[59],"Second,":[60],"3D":[61,83,132,156],"system":[62],"designs":[63],"address":[65],"thermal":[66,101],"issues":[67],"resulting":[68],"from":[69],"high-power":[70],"density.":[71],"work,":[74],"we":[75,106,137],"propose":[76],"methodology":[79,128,163],"NoC":[84,133],"architecture":[85],"that":[86],"not":[87],"only":[88,146],"satisfies":[89],"CPUs":[95],"GPUs,":[97],"but":[98],"reduces":[100],"hotspots.":[102],"To":[103],"end,":[105],"target":[107],"training":[109,135],"two":[111],"widely":[112],"convolutional":[114],"neural":[115],"networks":[116],"(CNN),":[117],"namely,":[118],"LeNet":[119],"CIFAR.":[121],"By":[122],"using":[123],"our":[124,161],"joint":[125],"performance-thermal":[126],"optimization":[127],"create":[130],"CNNs,":[136],"reduce":[138],"maximum":[140],"temperature":[141,166],"by":[142],"22%":[143],"while":[144],"incurring":[145],"5%":[147],"full-system":[148],"energy-delay-product":[149],"degradation":[150],"over":[151],"solely":[153],"performance":[154],"optimized":[155],"NoC.":[157],"This":[158],"demonstrates":[159],"that,":[160],"achieves":[164],"considerable":[165],"reduction":[167],"with":[168],"negligible":[169],"loss":[170],"performance.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
