{"id":"https://openalex.org/W3011370403","doi":"https://doi.org/10.1109/sips47522.2019.9020318","title":"Improving Reliability of ReRAM-Based DNN Implementation through Novel Weight Distribution","display_name":"Improving Reliability of ReRAM-Based DNN Implementation through Novel Weight Distribution","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3011370403","doi":"https://doi.org/10.1109/sips47522.2019.9020318","mag":"3011370403"},"language":"en","primary_location":{"id":"doi:10.1109/sips47522.2019.9020318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","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/A5100671149","display_name":"Jingtao Li","orcid":"https://orcid.org/0000-0003-4250-869X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingtao Li","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032613389","display_name":"Manqing Mao","orcid":"https://orcid.org/0000-0002-0028-6059"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manqing Mao","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025336372","display_name":"Chaitali Chakrabarti","orcid":"https://orcid.org/0000-0002-9859-7778"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaitali Chakrabarti","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1211,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51052338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"189","last_page":"194"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.996999979019165,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.8593695163726807},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.801398754119873},{"id":"https://openalex.org/keywords/static-random-access-memory","display_name":"Static random-access memory","score":0.6854386925697327},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6592389345169067},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4938276708126068},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.48755329847335815},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.46979913115501404},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.45689013600349426},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.42415085434913635},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4135904908180237},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3367202877998352},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.32154178619384766},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.2435893714427948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23688876628875732},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.17654013633728027},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17148369550704956},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.14009270071983337},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.110606849193573}],"concepts":[{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.8593695163726807},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.801398754119873},{"id":"https://openalex.org/C68043766","wikidata":"https://www.wikidata.org/wiki/Q267416","display_name":"Static random-access memory","level":2,"score":0.6854386925697327},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6592389345169067},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4938276708126068},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.48755329847335815},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.46979913115501404},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45689013600349426},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.42415085434913635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4135904908180237},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3367202877998352},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.32154178619384766},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2435893714427948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23688876628875732},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.17654013633728027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17148369550704956},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.14009270071983337},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.110606849193573},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sips47522.2019.9020318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1594170634","https://openalex.org/W1606347560","https://openalex.org/W1902934009","https://openalex.org/W1965318644","https://openalex.org/W1971319818","https://openalex.org/W1972127875","https://openalex.org/W2035570353","https://openalex.org/W2056507634","https://openalex.org/W2141016978","https://openalex.org/W2147735869","https://openalex.org/W2276892413","https://openalex.org/W2405920868","https://openalex.org/W2508602506","https://openalex.org/W2534720278","https://openalex.org/W2588034815","https://openalex.org/W2612375349","https://openalex.org/W2613989746","https://openalex.org/W2768104155","https://openalex.org/W2790964477","https://openalex.org/W2919115771","https://openalex.org/W2925935411","https://openalex.org/W2945788832","https://openalex.org/W4229786707","https://openalex.org/W6635557998","https://openalex.org/W6636358008","https://openalex.org/W6639703010","https://openalex.org/W6714058667"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4388700941"],"abstract_inverted_index":{"Binary":[0],"deep":[1],"neural":[2,155],"networks,":[3],"that":[4,42,105],"have":[5],"been":[6],"implemented":[7],"in":[8,22,68,98,129,159],"resistive":[9],"random":[10],"access":[11],"memory":[12],"(ReRAM)":[13],"for":[14],"storage":[15,52],"efficiency,":[16],"suffer":[17],"from":[18],"poor":[19],"recognition":[20,174],"performance":[21,175],"the":[23,55,58,74,90,96,106,140,147,152,160,166,180],"presence":[24,161],"of":[25,76,95,162],"hardware":[26,111,164],"errors.":[27,112],"This":[28],"paper":[29],"addresses":[30],"this":[31,122],"problem":[32],"by":[33,73,135,146],"deriving":[34],"a":[35,69,77,81,99,130,136,172,192,204],"novel":[36],"weight":[37,59,67],"distribution":[38],"and":[39,65,80,133,139,144,195,210],"representation":[40],"scheme":[41,169,182],"mitigates":[43],"errors":[44],"due":[45],"to":[46,92,110,120,151],"faulty":[47],"ReRAM":[48,148],"cells":[49],"with":[50],"minimal":[51],"overhead.":[53],"In":[54],"proposed":[56,119,167,181],"scheme,":[57],"matrix":[60],"is":[61,71,108,118],"partitioned":[62],"into":[63],"grains,":[64],"each":[66],"grain":[70,85,100],"represented":[72],"sum":[75],"multi-bit":[78],"mean":[79,91,125],"1-bit":[82],"deviation.":[83],"The":[84,124],"size":[86],"as":[87,89],"well":[88],"deviation":[93],"ratio":[94],"weights":[97],"can":[101],"be":[102],"chosen":[103],"such":[104],"network":[107,156],"resilient":[109],"A":[113],"hybrid":[114,168],"processing-in-memory":[115],"(PIM)":[116],"architecture":[117],"support":[121],"scheme.":[123],"values":[126],"are":[127,142],"stored":[128,143],"small":[131],"SRAM":[132],"processed":[134,145],"CMOS":[137],"unit,":[138],"deviations":[141],"unit.":[149],"Compared":[150],"baseline":[153],"binary":[154],"which":[157],"fails":[158],"severe":[163],"errors,":[165],"has":[170],"only":[171],"mild":[173],"degradation.":[176],"Simulation":[177],"results":[178],"show":[179],"achieves":[183],"97.84%":[184],"test":[185,197],"accuracy":[186,189,198,201],"(a":[187,199],"0.84%":[188],"drop)":[190,202],"on":[191,203],"MNIST":[193],"dataset,":[194],"88.07%":[196],"1.10%":[200],"CIFAR-10":[205],"dataset":[206],"under":[207],"9.04%":[208],"stuck-at-1":[209],"1.75%":[211],"stuck-at-0":[212],"faults.":[213]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
