{"id":"https://openalex.org/W2508804680","doi":"https://doi.org/10.1109/mwsym.2016.7540387","title":"Parallel matrix neural network training on cluster systems for dynamic FET modeling from large datasets","display_name":"Parallel matrix neural network training on cluster systems for dynamic FET modeling from large datasets","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2508804680","doi":"https://doi.org/10.1109/mwsym.2016.7540387","mag":"2508804680"},"language":"en","primary_location":{"id":"doi:10.1109/mwsym.2016.7540387","doi":"https://doi.org/10.1109/mwsym.2016.7540387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwsym.2016.7540387","pdf_url":null,"source":{"id":"https://openalex.org/S4210185519","display_name":"IEEE MTT-S International Microwave Symposium digest","issn_l":"0149-645X","issn":["0149-645X","2576-7216"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE MTT-S International Microwave Symposium (IMS)","raw_type":"proceedings-article"},"volume":null,"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/A5032345916","display_name":"Shunlu Zhang","orcid":"https://orcid.org/0000-0001-6054-4497","observed_orcids":["https://orcid.org/0000-0001-6054-4497"]},"institutions":[{"id":"https://openalex.org/I4210115805","display_name":"Keysight Technologies (United States)","ror":"https://ror.org/02903cd17","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115805"]},{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA","US"],"is_corresponding":false,"raw_author_name":"Shunlu Zhang","raw_affiliation_strings":["Department of Electronics, Carleton University, Ottawa, ON, Canada","Keysight Technologies, Inc., Santa Rosa, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics, Carleton University, Ottawa, ON, Canada","institution_ids":["https://openalex.org/I67031392"]},{"raw_affiliation_string":"Keysight Technologies, Inc., Santa Rosa, CA, USA","institution_ids":["https://openalex.org/I4210115805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023948588","display_name":"Jianjun Xu","orcid":"https://orcid.org/0000-0001-7668-0522","observed_orcids":["https://orcid.org/0000-0001-7668-0522"]},"institutions":[{"id":"https://openalex.org/I4210115805","display_name":"Keysight Technologies (United States)","ror":"https://ror.org/02903cd17","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115805"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianjun Xu","raw_affiliation_strings":["Keysight Technologies, Inc., Santa Rosa, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keysight Technologies, Inc., Santa Rosa, CA, USA","institution_ids":["https://openalex.org/I4210115805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100726170","display_name":"Qi\u2010Jun Zhang","orcid":"https://orcid.org/0000-0001-7852-5331","observed_orcids":["https://orcid.org/0000-0001-7852-5331"]},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Qi-Jun Zhang","raw_affiliation_strings":["Department of Electronics, Carleton University, Ottawa, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics, Carleton University, Ottawa, ON, Canada","institution_ids":["https://openalex.org/I67031392"]}]},{"author_position":"last","author":{"id":null,"display_name":"David E. Root","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210115805","display_name":"Keysight Technologies (United States)","ror":"https://ror.org/02903cd17","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115805"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David E. Root","raw_affiliation_strings":["Keysight Technologies, Inc., Santa Rosa, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keysight Technologies, Inc., Santa Rosa, CA, USA","institution_ids":["https://openalex.org/I4210115805"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6979,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.71295765,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.44780001044273376,"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/T10320","display_name":"Neural Networks and Applications","score":0.44780001044273376,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.43799999356269836,"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/T11248","display_name":"Advanced Power Amplifier Design","score":0.013700000010430813,"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/parallel-neural-network-training","display_name":"parallel neural network training","score":0.984000027179718},{"id":"https://openalex.org/keywords/cluster-computing","display_name":"cluster computing","score":0.8450000286102295},{"id":"https://openalex.org/keywords/gan-transistors","display_name":"GaN transistors","score":0.9879999756813049},{"id":"https://openalex.org/keywords/nonlinear-device-modeling","display_name":"nonlinear device modeling","score":0.5400000214576721},{"id":"https://openalex.org/keywords/training-data-distribution","display_name":"training data distribution","score":0.5820000171661377},{"id":"https://openalex.org/keywords/training-time-reduction","display_name":"training time reduction","score":0.7409999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.774459958076477},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7032806873321533},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5775712132453918},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.5725031495094299},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5353294610977173},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4917159676551819},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4881804585456848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45637571811676025},{"id":"https://openalex.org/C29140674","wikidata":"https://www.wikidata.org/wiki/Q206637","display_name":"Computer cluster","level":2,"score":0.454522043466568},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4502977728843689},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43845081329345703},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41114020347595215},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39611130952835083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.346922367811203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34294021129608154},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.28984081745147705},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.12693116068840027},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10354343056678772},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0853976309299469},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwsym.2016.7540387","doi":"https://doi.org/10.1109/mwsym.2016.7540387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwsym.2016.7540387","pdf_url":null,"source":{"id":"https://openalex.org/S4210185519","display_name":"IEEE MTT-S International Microwave Symposium digest","issn_l":"0149-645X","issn":["0149-645X","2576-7216"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE MTT-S International Microwave Symposium (IMS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W121843012","https://openalex.org/W1547274036","https://openalex.org/W2010132658","https://openalex.org/W2013939002","https://openalex.org/W2118383754","https://openalex.org/W2125237227"],"related_works":["https://openalex.org/W2484638864","https://openalex.org/W24708334","https://openalex.org/W2066613128","https://openalex.org/W2121954511","https://openalex.org/W2168984594","https://openalex.org/W1544811214","https://openalex.org/W2151297862","https://openalex.org/W7163600693","https://openalex.org/W88766802","https://openalex.org/W2581399127"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,30,35,75,96],"powerful":[4],"and":[5],"general":[6],"parallel":[7,14],"artificial":[8],"neural":[9],"network":[10],"training":[11,22,61,81,93],"technique":[12],"with":[13,44,79],"computing":[15],"on":[16,29],"cluster":[17,31],"systems.":[18],"Large":[19],"numbers":[20],"of":[21,49,98],"samples":[23],"are":[24],"distributed":[25],"to":[26,33,46,89],"multiple":[27],"computers":[28],"system":[32],"achieve":[34],"high":[36,76],"speed-up":[37],"for":[38,56],"training.":[39],"The":[40],"method":[41],"is":[42,63,87],"evaluated":[43],"respect":[45],"two":[47],"examples":[48],"an":[50,69],"advanced":[51,72],"dynamic":[52],"nonlinear":[53],"simulation":[54],"model":[55],"GaN":[57],"transistors":[58],"where":[59],"the":[60,84,91],"set":[62],"measured":[64],"large-signal":[65],"waveform":[66],"data":[67],"from":[68],"NVNA.":[70],"For":[71],"models":[73],"in":[74],"dimensional":[77],"space":[78],"large":[80],"sample":[82],"size,":[83],"proposed":[85],"approach":[86],"demonstrated":[88],"reduce":[90],"total":[92],"time":[94],"by":[95],"factor":[97],"35.":[99]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-09-16T00:00:00"}
