{"id":"https://openalex.org/W2512985564","doi":"https://doi.org/10.1109/tmscs.2016.2598742","title":"Design of Resistive Synaptic Array for Implementing On-Chip Sparse Learning","display_name":"Design of Resistive Synaptic Array for Implementing On-Chip Sparse Learning","publication_year":2016,"publication_date":"2016-08-09","ids":{"openalex":"https://openalex.org/W2512985564","doi":"https://doi.org/10.1109/tmscs.2016.2598742","mag":"2512985564"},"language":"en","primary_location":{"id":"doi:10.1109/tmscs.2016.2598742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmscs.2016.2598742","pdf_url":null,"source":{"id":"https://openalex.org/S4210201583","display_name":"IEEE Transactions on Multi-Scale Computing Systems","issn_l":"2332-7766","issn":["2332-7766","2372-207X"],"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 Multi-Scale Computing 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/A5011351663","display_name":"Pai-Yu Chen","orcid":"https://orcid.org/0000-0002-9146-2192"},"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":"Pai-Yu Chen","raw_affiliation_strings":["Arizona State University, Tempe, AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049362025","display_name":"Ligang Gao","orcid":"https://orcid.org/0000-0002-8886-3518"},"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":"Ligang Gao","raw_affiliation_strings":["Arizona State University, Tempe, AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054894631","display_name":"Shimeng Yu","orcid":"https://orcid.org/0000-0002-0068-3652"},"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":"Shimeng Yu","raw_affiliation_strings":["Arizona State University, Tempe, AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":3.2076,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.92410057,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2","issue":"4","first_page":"257","last_page":"264"},"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7068392634391785},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6450657844543457},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6211733818054199},{"id":"https://openalex.org/keywords/resistive-touchscreen","display_name":"Resistive touchscreen","score":0.5325713753700256},{"id":"https://openalex.org/keywords/parasitic-extraction","display_name":"Parasitic extraction","score":0.4877651333808899},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.46687787771224976},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.4569672644138336},{"id":"https://openalex.org/keywords/synaptic-weight","display_name":"Synaptic weight","score":0.454456090927124},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4401698410511017},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3644202947616577},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.328133225440979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3259451389312744},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24139681458473206},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12188157439231873}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7068392634391785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6450657844543457},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6211733818054199},{"id":"https://openalex.org/C6899612","wikidata":"https://www.wikidata.org/wiki/Q852911","display_name":"Resistive touchscreen","level":2,"score":0.5325713753700256},{"id":"https://openalex.org/C159818811","wikidata":"https://www.wikidata.org/wiki/Q7135947","display_name":"Parasitic extraction","level":2,"score":0.4877651333808899},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.46687787771224976},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.4569672644138336},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.454456090927124},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4401698410511017},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3644202947616577},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.328133225440979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3259451389312744},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24139681458473206},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12188157439231873},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmscs.2016.2598742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmscs.2016.2598742","pdf_url":null,"source":{"id":"https://openalex.org/S4210201583","display_name":"IEEE Transactions on Multi-Scale Computing Systems","issn_l":"2332-7766","issn":["2332-7766","2372-207X"],"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 Multi-Scale Computing Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6239287657","display_name":null,"funder_award_id":"1552687","funder_id":"https://openalex.org/F4320337387","funder_display_name":"Division of Computing and Communication Foundations"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337387","display_name":"Division of Computing and Communication Foundations","ror":"https://ror.org/01mng8331"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1596035946","https://openalex.org/W1937359183","https://openalex.org/W1984750698","https://openalex.org/W1989012887","https://openalex.org/W1994589723","https://openalex.org/W1995839907","https://openalex.org/W1996511377","https://openalex.org/W2010778124","https://openalex.org/W2014402164","https://openalex.org/W2016922062","https://openalex.org/W2026145098","https://openalex.org/W2028868915","https://openalex.org/W2040429262","https://openalex.org/W2049930603","https://openalex.org/W2057055374","https://openalex.org/W2069552454","https://openalex.org/W2095725009","https://openalex.org/W2113606819","https://openalex.org/W2116360511","https://openalex.org/W2117422822","https://openalex.org/W2138913040","https://openalex.org/W2145889472","https://openalex.org/W2152514935","https://openalex.org/W2153635508","https://openalex.org/W2288519604","https://openalex.org/W2319920447","https://openalex.org/W2398088337","https://openalex.org/W2509746188","https://openalex.org/W4236709213","https://openalex.org/W4249616338","https://openalex.org/W6676903177","https://openalex.org/W6700264148","https://openalex.org/W6712374975","https://openalex.org/W6725693772"],"related_works":["https://openalex.org/W2809732489","https://openalex.org/W3137378424","https://openalex.org/W4287780255","https://openalex.org/W3023361272","https://openalex.org/W3035640865","https://openalex.org/W4287639722","https://openalex.org/W2804040790","https://openalex.org/W4385828527","https://openalex.org/W2988574309","https://openalex.org/W3163773863"],"abstract_inverted_index":{"The":[0,130,170],"resistive":[1],"cross-point":[2],"array":[3,49],"architecture":[4],"has":[5],"been":[6],"proposed":[7],"for":[8],"on-chip":[9],"implementation":[10],"of":[11,75,106,120,145,196],"weighted":[12,52,97],"sum":[13,98],"and":[14,34,40,48,71,157,160],"weight":[15,38,46,90,178],"update":[16,91],"operations":[17],"in":[18,37,51,89,96,177,200],"neuro-inspired":[19],"learning":[20,29],"algorithms.":[21],"However,":[22],"several":[23],"limiting":[24],"factors":[25],"potentially":[26],"hamper":[27],"the":[28,32,41,73,80,86,102,117,143,154,174,186],"accuracy,":[30],"including":[31],"nonlinearity":[33],"device":[35,146],"variations":[36],"update,":[39],"read":[42],"noise,":[43],"limited":[44],"ON/OFF":[45],"ratio":[47],"parasitics":[50],"sum.":[53],"With":[54,104,180],"unsupervised":[55],"sparse":[56],"coding":[57],"as":[58],"a":[59,149],"case":[60],"study":[61,114],"algorithm,":[62],"this":[63,134],"paper":[64],"employs":[65],"device-algorithm":[66],"co-design":[67],"methodologies":[68],"to":[69,101,127,132,141,152,164,190],"quantify":[70],"mitigate":[72,133],"impact":[74,144],"these":[76,184],"non-ideal":[77],"properties":[78,88,182],"on":[79],"accuracy.":[81,103],"Our":[82],"analysis":[83],"shows":[84,115],"that":[85,116],"realistic":[87,107,197],"are":[92,99],"tolerable,":[93],"while":[94],"those":[95],"detrimental":[100],"calibration":[105],"synaptic":[108,198],"behaviors":[109],"from":[110,125],"experimental":[111],"data,":[112],"our":[113],"recognition":[118],"accuracy":[119,135,187],"MNIST":[121],"handwriting":[122],"digits":[123],"degrades":[124],"\u223c96":[126],"\u223c30":[128],"percent.":[129],"strategies":[131],"loss":[136],"include":[137],"1)":[138],"redundant":[139],"cells":[140],"alleviate":[142],"variations;":[147],"2)":[148],"dummy":[150],"column":[151],"eliminate":[153],"off-state":[155],"current;":[156],"3)":[158],"selector":[159,171],"larger":[161],"wire":[162],"width":[163],"reduce":[165],"IR":[166],"drop":[167],"along":[168],"interconnects.":[169],"also":[172],"reduces":[173],"leakage":[175],"power":[176],"update.":[179],"improved":[181],"by":[183],"strategies,":[185],"increases":[188],"back":[189],"\u223c95":[191],"percent,":[192],"enabling":[193],"reliable":[194],"integration":[195],"devices":[199],"neuromorphic":[201],"systems.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}