{"id":"https://openalex.org/W3107855596","doi":"https://doi.org/10.1109/tvlsi.2020.3037166","title":"Memory-Augmented Neural Networks on FPGA for Real-Time and Energy-Efficient Question Answering","display_name":"Memory-Augmented Neural Networks on FPGA for Real-Time and Energy-Efficient Question Answering","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3107855596","doi":"https://doi.org/10.1109/tvlsi.2020.3037166","mag":"3107855596"},"language":"en","primary_location":{"id":"doi:10.1109/tvlsi.2020.3037166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2020.3037166","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"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 Very Large Scale Integration (VLSI) Systems","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/A5058191948","display_name":"Seongsik Park","orcid":"https://orcid.org/0000-0003-4281-4080"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seongsik Park","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4281-4080","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102813831","display_name":"Jaehee Jang","orcid":"https://orcid.org/0000-0003-0322-5654"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaehee Jang","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0322-5654","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007499795","display_name":"Seijoon Kim","orcid":"https://orcid.org/0000-0001-6318-7050"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seijoon Kim","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058165461","display_name":"Byunggook Na","orcid":"https://orcid.org/0000-0002-0459-4921"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byunggook Na","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086877012","display_name":"Sungroh Yoon","orcid":"https://orcid.org/0000-0002-2367-197X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungroh Yoon","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2367-197X","affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058191948"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.6795,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.775559,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"29","issue":"1","first_page":"162","last_page":"175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9977999925613403,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9977999925613403,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958999752998352,"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/T10028","display_name":"Topic Modeling","score":0.9922999739646912,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8567093014717102},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7521708011627197},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6595882177352905},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5338394045829773},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.46748822927474976},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.46412065625190735},{"id":"https://openalex.org/keywords/auxiliary-memory","display_name":"Auxiliary memory","score":0.4473428726196289},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3694790303707123},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.33120402693748474},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.29363805055618286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21453243494033813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8567093014717102},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7521708011627197},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6595882177352905},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5338394045829773},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.46748822927474976},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.46412065625190735},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.4473428726196289},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3694790303707123},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.33120402693748474},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.29363805055618286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21453243494033813},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvlsi.2020.3037166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2020.3037166","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"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 Very Large Scale Integration (VLSI) Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1492347181","https://openalex.org/W1525961042","https://openalex.org/W1558797106","https://openalex.org/W1902934009","https://openalex.org/W1987971958","https://openalex.org/W1999085092","https://openalex.org/W2116249769","https://openalex.org/W2131462252","https://openalex.org/W2131494463","https://openalex.org/W2131744502","https://openalex.org/W2145482038","https://openalex.org/W2153579005","https://openalex.org/W2267635276","https://openalex.org/W2276486856","https://openalex.org/W2285660444","https://openalex.org/W2289252105","https://openalex.org/W2294059674","https://openalex.org/W2400680200","https://openalex.org/W2472819217","https://openalex.org/W2530887700","https://openalex.org/W2540279855","https://openalex.org/W2582905775","https://openalex.org/W2585720638","https://openalex.org/W2606722458","https://openalex.org/W2750846973","https://openalex.org/W2765235648","https://openalex.org/W2794986474","https://openalex.org/W2896457183","https://openalex.org/W2937485308","https://openalex.org/W2949591530","https://openalex.org/W2950527759","https://openalex.org/W2951008357","https://openalex.org/W2952556884","https://openalex.org/W2962820060","https://openalex.org/W2962985038","https://openalex.org/W2963114950","https://openalex.org/W2963341956","https://openalex.org/W2963355595","https://openalex.org/W2963448850","https://openalex.org/W2963674932","https://openalex.org/W2964091467","https://openalex.org/W2980020162","https://openalex.org/W2980200167","https://openalex.org/W4242709165","https://openalex.org/W4294170691","https://openalex.org/W4294611325","https://openalex.org/W4295262505","https://openalex.org/W4297824512","https://openalex.org/W4303633609","https://openalex.org/W4307979480","https://openalex.org/W6631399359","https://openalex.org/W6633532678","https://openalex.org/W6638318767","https://openalex.org/W6639703010","https://openalex.org/W6679224782","https://openalex.org/W6679775712","https://openalex.org/W6679844565","https://openalex.org/W6682691769","https://openalex.org/W6684821475","https://openalex.org/W6693397755","https://openalex.org/W6696761078","https://openalex.org/W6712948638","https://openalex.org/W6720057410","https://openalex.org/W6728916552","https://openalex.org/W6732742072","https://openalex.org/W6744013678","https://openalex.org/W6746064817","https://openalex.org/W6747381273","https://openalex.org/W6755207826"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Memory-augmented":[0],"neural":[1,75],"networks":[2,76],"(MANNs)":[3],"were":[4],"introduced":[5],"to":[6,83,91,171,189],"handle":[7],"long-term":[8],"dependent":[9],"data":[10,168],"efficiently.":[11],"MANNs":[12,43,89,124,187],"have":[13,38,77],"shown":[14],"promising":[15],"results":[16],"in":[17,44],"question":[18],"answering":[19,27],"(QA)":[20],"tasks":[21,99],"that":[22],"require":[23],"holding":[24],"contexts":[25],"for":[26,33,72,103,165],"a":[28],"given":[29],"question.":[30],"As":[31],"demands":[32],"QA":[34,98,142],"on":[35,64,110,125,154],"edge":[36],"devices":[37],"increased,":[39],"the":[40,86,106,111,115,130,139,151,159,166,175,184],"utilization":[41],"of":[42,56,97,108,123,141,183,186,199],"resource-constrained":[45],"environments":[46],"has":[47],"become":[48],"important.":[49],"To":[50,113,127,144],"achieve":[51],"fast":[52,135],"and":[53,157,162,180,192],"energy-efficient":[54],"inference":[55,109,121,136,185],"MANNs,":[57],"we":[58,118,133,149],"can":[59],"exploit":[60],"application-specific":[61],"hardware":[62],"accelerators":[63,71,87],"field-programmable":[65],"gate":[66],"arrays":[67],"(FPGAs).":[68],"Although":[69],"several":[70],"conventional":[73],"deep":[74],"been":[78],"designed,":[79],"it":[80],"is":[81],"difficult":[82],"efficiently":[84],"utilize":[85,129],"with":[88,197],"due":[90],"different":[92],"requirements.":[93],"In":[94],"addition,":[95],"characteristics":[96],"should":[100],"be":[101],"considered":[102],"further":[104],"improving":[105],"efficiency":[107,182],"accelerators.":[112],"address":[114],"aforementioned":[116],"issues,":[117],"propose":[119],"an":[120,155],"accelerator":[122],"FPGA.":[126],"fully":[128],"proposed":[131,147,152,176],"accelerator,":[132],"introduce":[134],"methods":[137,177],"considering":[138],"features":[140],"tasks.":[143],"evaluate":[145],"our":[146,172],"approach,":[148],"implemented":[150],"architecture":[153],"FPGA":[156],"measured":[158],"execution":[160],"time":[161],"energy":[163,181],"consumption":[164],"bAbI":[167],"set.":[169],"According":[170],"thorough":[173],"experiments,":[174],"improved":[178],"speed":[179],"up":[188],"about":[190],"25.6":[191],"28.4":[193],"times,":[194],"respectively,":[195],"compared":[196],"those":[198],"CPU.":[200]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}