{"id":"https://openalex.org/W2989935501","doi":"https://doi.org/10.1109/icrc.2019.8914713","title":"Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks","display_name":"Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2989935501","doi":"https://doi.org/10.1109/icrc.2019.8914713","mag":"2989935501"},"language":"en","primary_location":{"id":"doi:10.1109/icrc.2019.8914713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icrc.2019.8914713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Rebooting Computing (ICRC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.10636","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wen Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wen Ma","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pi-Feng Chiu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pi-Feng Chiu","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Won Ho Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Won Ho Choi","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Minghai Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minghai Qin","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Daniel Bedau","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Bedau","raw_affiliation_strings":["Western Digital Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"last","author":{"id":null,"display_name":"Martin Lueker-Boden","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Lueker-Boden","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210121352"],"apc_list":null,"apc_paid":null,"fwci":0.242,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57082213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"9908 lncs","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","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/T10320","display_name":"Neural Networks and Applications","score":0.9986000061035156,"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/quantization","display_name":"Quantization (signal processing)","score":0.6919000148773193},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6689000129699707},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.6401000022888184},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5825999975204468},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4952999949455261},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4442000091075897},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.42489999532699585},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.3594000041484833},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3495999872684479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7756999731063843},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6919000148773193},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6689000129699707},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.6401000022888184},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5825999975204468},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4952999949455261},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4442000091075897},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4375999867916107},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38089999556541443},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.3594000041484833},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C135402231","wikidata":"https://www.wikidata.org/wiki/Q898440","display_name":"Dissipation","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30079999566078186},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C131017901","wikidata":"https://www.wikidata.org/wiki/Q170451","display_name":"Logic gate","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2606000006198883}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icrc.2019.8914713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icrc.2019.8914713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Rebooting Computing (ICRC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.10636","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.10636","pdf_url":"https://arxiv.org/pdf/2002.10636","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2002.10636","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.10636","pdf_url":"https://arxiv.org/pdf/2002.10636","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W1632114991","https://openalex.org/W1942251340","https://openalex.org/W2016922062","https://openalex.org/W2040687898","https://openalex.org/W2064675550","https://openalex.org/W2069552454","https://openalex.org/W2107712601","https://openalex.org/W2130360162","https://openalex.org/W2138913040","https://openalex.org/W2163630896","https://openalex.org/W2334364695","https://openalex.org/W2399958287","https://openalex.org/W2507278625","https://openalex.org/W2508602506","https://openalex.org/W2518281301","https://openalex.org/W2743575928","https://openalex.org/W2782046614","https://openalex.org/W2783525259","https://openalex.org/W2811080765","https://openalex.org/W2895545069","https://openalex.org/W2919115771","https://openalex.org/W2962949994","https://openalex.org/W2962962672","https://openalex.org/W2963059095","https://openalex.org/W2966081953","https://openalex.org/W6693397755","https://openalex.org/W6696934422","https://openalex.org/W6698200048","https://openalex.org/W6727208969","https://openalex.org/W6748316359","https://openalex.org/W6748714585","https://openalex.org/W6764473562"],"related_works":[],"abstract_inverted_index":{"In":[0,62,92,104],"cloud":[1],"and":[2,38,119,133,145,165,195,206],"edge":[3,14],"computing":[4,193,219],"models,":[5,118],"it":[6],"is":[7,49],"important":[8],"that":[9,140],"compute":[10],"devices":[11],"at":[12,59,189],"the":[13,60,101,110,120],"be":[15,169],"as":[16,19,156],"power":[17,66,102],"efficient":[18],"possible.":[20],"Long":[21],"short-term":[22],"memory":[23,88],"(LSTM)":[24],"neural":[25],"networks":[26],"have":[27,138],"been":[28,77],"widely":[29],"used":[30],"for":[31,45,52,55,185],"natural":[32],"language":[33],"processing,":[34],"time":[35],"series":[36],"prediction":[37],"many":[39],"other":[40,209],"sequential":[41],"data":[42,70],"tasks.":[43],"Thus,":[44],"these":[46,122],"applications":[47],"there":[48,75],"increasing":[50],"need":[51],"low-power":[53],"accelerators":[54],"LSTM":[56,117,153,176,183],"model":[57],"inference":[58,73,186],"edge.":[61],"order":[63],"to":[64,69,116,150,216,222],"reduce":[65],"dissipation":[67],"due":[68],"transfers":[71],"within":[72,172],"devices,":[74],"has":[76,187],"significant":[78],"interest":[79],"in":[80,125],"accelerating":[81],"vector-matrix":[82],"multiplication":[83],"(VMM)":[84],"operations":[85],"using":[86],"non-volatile":[87],"(NVM)":[89],"weight":[90,166],"arrays.":[91],"NVM":[93,143,214],"array-based":[94],"hardware,":[95],"reduced":[96],"bit-widths":[97],"also":[98],"significantly":[99],"increases":[100],"efficiency.":[103],"this":[105],"paper,":[106],"we":[107],"focus":[108],"on":[109,213],"application":[111],"of":[112,127,161,180],"quantization-aware":[113],"training":[114],"algorithm":[115],"benefits":[121],"models":[123],"bring":[124],"terms":[126],"resilience":[128],"against":[129],"both":[130],"quantization":[131,163],"error":[132,231],"analog":[134],"device":[135],"noise.":[136],"We":[137],"shown":[139,188],"only":[141],"4-bit":[142,146],"weights":[144],"ADC/DACs":[147],"are":[148],"needed":[149],"produce":[151],"equivalent":[152],"network":[154],"performance":[155],"floating-point":[157],"baseline.":[158],"Reasonable":[159],"levels":[160],"ADC":[162],"noise":[164,167],"can":[168],"naturally":[170],"tolerated":[171],"our":[173,181],"NVM-based":[174],"quantized":[175],"network.":[177],"Benchmark":[178],"analysis":[179],"proposed":[182],"accelerator":[184],"least":[190],"2.4\u00d7":[191],"better":[192],"efficiency":[194,199,220],"40\u00d7":[196],"higher":[197,218,230],"area":[198],"than":[200],"traditional":[201],"digital":[202],"approaches":[203,211],"(GPU,":[204],"FPGA,":[205],"ASIC).":[207],"Some":[208],"novel":[210],"based":[212],"promise":[215],"deliver":[217],"(up":[221],"\u00d74.7)":[223],"but":[224],"require":[225],"larger":[226],"arrays":[227],"with":[228],"potential":[229],"rates.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2019-12-05T00:00:00"}
