{"id":"https://openalex.org/W3160017297","doi":"https://doi.org/10.1109/tc.2021.3078316","title":"A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type","display_name":"A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type","publication_year":2021,"publication_date":"2021-05-08","ids":{"openalex":"https://openalex.org/W3160017297","doi":"https://doi.org/10.1109/tc.2021.3078316","mag":"3160017297"},"language":"en","primary_location":{"id":"doi:10.1109/tc.2021.3078316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2021.3078316","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"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 Transactions on Computers","raw_type":"journal-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/A5034558336","display_name":"Seungkyu Choi","orcid":"https://orcid.org/0000-0002-3125-9707"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seungkyu Choi","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","[Department of Electrical Engineering, KAIST, 34968 Daejeon, N/A, Korea (the Republic of), (e-mail: skchoi@mvlsi.kaist.ac.kr)]"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"[Department of Electrical Engineering, KAIST, 34968 Daejeon, N/A, Korea (the Republic of), (e-mail: skchoi@mvlsi.kaist.ac.kr)]","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072316644","display_name":"Jaekang Shin","orcid":"https://orcid.org/0000-0002-5943-1599"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaekang Shin","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","[Department of Electrical Engineering, KAIST, 34968 Daejeon, N/A, Korea (the Republic of), (e-mail: jkshin@mvlsi.kaist.ac.kr)]"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"[Department of Electrical Engineering, KAIST, 34968 Daejeon, N/A, Korea (the Republic of), (e-mail: jkshin@mvlsi.kaist.ac.kr)]","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052390471","display_name":"Lee\u2010Sup Kim","orcid":"https://orcid.org/0000-0001-9585-4591"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Lee-Sup Kim","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","[Electrical Engineering, KAIST, Daejeon, Daejeon, Korea (the Republic of), (e-mail: leesup@kaist.ac.kr)]"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"[Electrical Engineering, KAIST, Daejeon, Daejeon, Korea (the Republic of), (e-mail: leesup@kaist.ac.kr)]","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034558336"],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":0.6725,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.70464052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"71","issue":"5","first_page":"1216","last_page":"1229"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9958999752998352,"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":0.8200998306274414},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6913579702377319},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6586302518844604},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6035008430480957},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.5921044945716858},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5850890874862671},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5573792457580566},{"id":"https://openalex.org/keywords/datapath","display_name":"Datapath","score":0.5247002840042114},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4999511241912842},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.47179311513900757},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4713838994503021},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.450278639793396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4501681923866272},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.44674360752105713},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.36847031116485596},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34175431728363037},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3097238540649414},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18500521779060364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8200998306274414},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6913579702377319},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6586302518844604},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6035008430480957},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.5921044945716858},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5850890874862671},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5573792457580566},{"id":"https://openalex.org/C2781198647","wikidata":"https://www.wikidata.org/wiki/Q1633673","display_name":"Datapath","level":2,"score":0.5247002840042114},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4999511241912842},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.47179311513900757},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4713838994503021},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.450278639793396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4501681923866272},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.44674360752105713},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.36847031116485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34175431728363037},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3097238540649414},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18500521779060364},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tc.2021.3078316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2021.3078316","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"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 Transactions on Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.75,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G2389063100","display_name":null,"funder_award_id":"2020R1A2B5B02002690","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320315121","display_name":"Samsung Advanced Institute of Technology","ror":null},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1999085092","https://openalex.org/W2048266589","https://openalex.org/W2155385791","https://openalex.org/W2194775991","https://openalex.org/W2469490737","https://openalex.org/W2525778437","https://openalex.org/W2625457103","https://openalex.org/W2725159389","https://openalex.org/W2751477244","https://openalex.org/W2753301142","https://openalex.org/W2783000019","https://openalex.org/W2786771851","https://openalex.org/W2787513570","https://openalex.org/W2794478957","https://openalex.org/W2809624076","https://openalex.org/W2883283076","https://openalex.org/W2884360532","https://openalex.org/W2896457183","https://openalex.org/W2898985762","https://openalex.org/W2900327659","https://openalex.org/W2921918777","https://openalex.org/W2923014074","https://openalex.org/W2944850220","https://openalex.org/W2945992628","https://openalex.org/W2946955515","https://openalex.org/W2949866178","https://openalex.org/W2949870694","https://openalex.org/W2962761403","https://openalex.org/W2963367920","https://openalex.org/W2971946864","https://openalex.org/W3024621361","https://openalex.org/W3040850704","https://openalex.org/W3177265267","https://openalex.org/W4240745123","https://openalex.org/W4288346545","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6677651945","https://openalex.org/W6679349572","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6703116779","https://openalex.org/W6713134421","https://openalex.org/W6720242923","https://openalex.org/W6727690538","https://openalex.org/W6743755670","https://openalex.org/W6744700018","https://openalex.org/W6748224102","https://openalex.org/W6748319235","https://openalex.org/W6753069482","https://openalex.org/W6755207826","https://openalex.org/W6756118020","https://openalex.org/W6762484958","https://openalex.org/W6763653508","https://openalex.org/W6767032739"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W3091976719","https://openalex.org/W2517027266","https://openalex.org/W4378806073","https://openalex.org/W2752721426","https://openalex.org/W2524802307","https://openalex.org/W3160017297"],"abstract_inverted_index":{"As":[0],"deep":[1,38,74],"learning":[2,39],"applications":[3],"often":[4],"encounter":[5],"accuracy":[6],"degradation":[7],"due":[8],"to":[9,123,131,149],"the":[10,27,51,57,61,109,138,167,186],"distorted":[11],"inputs":[12],"from":[13],"a":[14,36,73,144],"variety":[15],"of":[16,59,82,102,114,170,195,202],"environmental":[17],"conditions,":[18],"training":[19,55,77,179,198],"with":[20,111,185],"personal":[21],"data":[22,110],"has":[23,41],"become":[24],"essential":[25],"for":[26,54,64,86,96,137,161],"edge":[28],"devices.":[29],"Hence,":[30],"&#x2018;training":[31],"on":[32,105],"edge&#x2019;":[33],"by":[34],"supporting":[35,79],"trainable":[37],"accelerator":[40,78],"been":[42],"actively":[43],"studied.":[44],"Nevertheless,":[45],"previous":[46,168],"research":[47],"does":[48],"not":[49],"consider":[50],"fundamental":[52],"datapath":[53],"and":[56,84,116,134,158,180,199],"importance":[58],"retaining":[60],"high":[62,103,159,175],"performance":[63,104],"inference":[65],"tasks.":[66],"In":[67],"this":[68],"work,":[69],"we":[70,107,153],"propose":[71],"NeuroFlix,":[72],"neural":[75],"network":[76],"heterogeneous":[80],"data-type":[81],"floating-":[83],"fixed-point":[85,113],"input":[87,98,140],"operands.":[88],"From":[89],"two":[90],"perspectives:":[91],"1)":[92],"separate":[93,151],"precision":[94],"decision":[95],"each":[97],"data,":[99],"2)":[100],"maintenance":[101],"inference,":[106],"configure":[108],"low-bit":[112],"activation/weight":[115],"floating-point":[117,146],"based":[118,147],"error":[119],"gradient":[120],"securing":[121],"up":[122],"half-precision.":[124],"A":[125],"novel":[126],"MAC":[127],"architecture":[128,157],"is":[129],"designed":[130],"compute":[132],"low-":[133],"high-precision":[135],"modes":[136],"different":[139],"combinations.":[141],"By":[142,182],"substituting":[143],"high-cost":[145],"addition":[148],"brick-level":[150],"accumulations,":[152],"realize":[154],"both":[155,178],"area-efficient":[156],"throughput":[160],"low-precision":[162],"computation.":[163],"Consequently,":[164],"NeuroFlix":[165],"outperforms":[166],"architectures":[169],"state-of-the-art":[171],"configurations":[172],"proving":[173],"its":[174],"efficiency":[176],"in":[177],"inference.":[181,207],"also":[183],"comparing":[184],"off-the-shelf":[187],"bfloat16-based":[188],"accelerator,":[189],"it":[190],"achieves":[191],"1.2":[192],"&#x00D7;/2.0":[193],"&#x00D7;":[194,205],"speedup/energy-efficiency":[196],"at":[197,206],"further":[200],"enhancement":[201],"3.6":[203],"&#x00D7;/4.5":[204]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
