{"id":"https://openalex.org/W3174309743","doi":"https://doi.org/10.1109/aicas51828.2021.9458528","title":"MRAM-based BER resilient Quantized edge-AI Networks for Harsh Industrial Conditions","display_name":"MRAM-based BER resilient Quantized edge-AI Networks for Harsh Industrial Conditions","publication_year":2021,"publication_date":"2021-06-06","ids":{"openalex":"https://openalex.org/W3174309743","doi":"https://doi.org/10.1109/aicas51828.2021.9458528","mag":"3174309743"},"language":"en","primary_location":{"id":"doi:10.1109/aicas51828.2021.9458528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicas51828.2021.9458528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)","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/A5066176656","display_name":"Vivek Parmar","orcid":"https://orcid.org/0000-0001-7380-0816"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vivek Parmar","raw_affiliation_strings":["Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008011360","display_name":"Manan Suri","orcid":"https://orcid.org/0000-0003-1417-3570"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manan Suri","raw_affiliation_strings":["CYRAN AI Solutions, India","Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"CYRAN AI Solutions, India","institution_ids":[]},{"raw_affiliation_string":"Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110779470","display_name":"Kazutaka Yamane","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136567","display_name":"GlobalFoundries (Singapore)","ror":"https://ror.org/03whnfd14","country_code":"SG","type":"company","lineage":["https://openalex.org/I35662394","https://openalex.org/I4210136567"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Kazutaka Yamane","raw_affiliation_strings":["GLOBALFOUNDRIES, Singapore"],"affiliations":[{"raw_affiliation_string":"GLOBALFOUNDRIES, Singapore","institution_ids":["https://openalex.org/I4210136567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101764336","display_name":"Tae Young Lee","orcid":"https://orcid.org/0009-0004-8849-4822"},"institutions":[{"id":"https://openalex.org/I4210136567","display_name":"GlobalFoundries (Singapore)","ror":"https://ror.org/03whnfd14","country_code":"SG","type":"company","lineage":["https://openalex.org/I35662394","https://openalex.org/I4210136567"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Taeyoung Lee","raw_affiliation_strings":["GLOBALFOUNDRIES, Singapore"],"affiliations":[{"raw_affiliation_string":"GLOBALFOUNDRIES, Singapore","institution_ids":["https://openalex.org/I4210136567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089567889","display_name":"N. L. Chung","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136567","display_name":"GlobalFoundries (Singapore)","ror":"https://ror.org/03whnfd14","country_code":"SG","type":"company","lineage":["https://openalex.org/I35662394","https://openalex.org/I4210136567"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Nyuk Leong Chung","raw_affiliation_strings":["GLOBALFOUNDRIES, Singapore"],"affiliations":[{"raw_affiliation_string":"GLOBALFOUNDRIES, Singapore","institution_ids":["https://openalex.org/I4210136567"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103486413","display_name":"V. B. Naik","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136567","display_name":"GlobalFoundries (Singapore)","ror":"https://ror.org/03whnfd14","country_code":"SG","type":"company","lineage":["https://openalex.org/I35662394","https://openalex.org/I4210136567"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Vinayak Bharat Naik","raw_affiliation_strings":["GLOBALFOUNDRIES, Singapore"],"affiliations":[{"raw_affiliation_string":"GLOBALFOUNDRIES, Singapore","institution_ids":["https://openalex.org/I4210136567"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5066176656"],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":0.2005,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49675617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9976999759674072,"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":0.9976999759674072,"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":0.9976000189781189,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9922999739646912,"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/mnist-database","display_name":"MNIST database","score":0.7412193417549133},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6955565810203552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6178562641143799},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5654325485229492},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.529853105545044},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5227394700050354},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5121610164642334},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5022029876708984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5009613037109375},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.49719002842903137},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.48398900032043457},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41973668336868286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.361594557762146},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3341861367225647},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.12047982215881348}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7412193417549133},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6955565810203552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6178562641143799},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5654325485229492},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.529853105545044},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5227394700050354},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5121610164642334},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5022029876708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5009613037109375},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.49719002842903137},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.48398900032043457},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41973668336868286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.361594557762146},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3341861367225647},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.12047982215881348},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aicas51828.2021.9458528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aicas51828.2021.9458528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1529951006","https://openalex.org/W2585407525","https://openalex.org/W2778345336","https://openalex.org/W2790511620","https://openalex.org/W2898994846","https://openalex.org/W2912505786","https://openalex.org/W2941931973","https://openalex.org/W2945639226","https://openalex.org/W2950326227","https://openalex.org/W2963029056","https://openalex.org/W2993805065","https://openalex.org/W3139001423"],"related_works":["https://openalex.org/W1908107260","https://openalex.org/W1952005211","https://openalex.org/W3166492421","https://openalex.org/W3176282186","https://openalex.org/W2973622361","https://openalex.org/W4387489555","https://openalex.org/W4395024198","https://openalex.org/W4293053895","https://openalex.org/W4288024917","https://openalex.org/W2983364019"],"abstract_inverted_index":{"We":[0,34],"investigate":[1],"Edge-AI":[2],"Inference":[3],"(EAI)":[4],"architectures":[5],"based":[6,72],"on":[7,59,111],"22nm":[8],"FD-SOI":[9],"embedded-MRAM":[10],"(eMRAM)":[11],"using":[12],"quantized":[13],"neural":[14],"networks":[15],"(QNN)":[16],"for":[17,101,106],"inference":[18,120],"applications":[19],"in":[20,57],"harsh":[21],"industrial":[22],"conditions":[23],"having":[24],"strong":[25],"magnetic":[26,77],"field":[27],"and":[28,47,94,103,119],"wide":[29],"operating":[30],"temperature":[31],"(-40~125":[32],"\u00b0C).":[33],"achieved":[35],"best":[36],"case":[37],"test":[38],"accuracy":[39,121],"of":[40,66,79,92,98],"98.99%":[41],"with":[42,49],"Quantized-Convolutional":[43],"Neural":[44],"Network":[45],"(QCNN)":[46],"89.94%":[48],"Quantized-Multi-layer":[50],"Perceptron":[51],"(QMLP)":[52],"surpassing":[53],"prior":[54],"reported":[55],"results":[56],"literature":[58],"MNIST":[60],"dataset.":[61],"By":[62],"exploiting":[63],"BER":[64],"resilience":[65],"QNN,":[67],"we":[68],"show":[69],"that":[70],"eMRAM":[71],"EAI":[73],"offers":[74],"a":[75],"superior":[76],"immunity":[78],"\u2248":[80,99,104],"700":[81],"Oe":[82],"at":[83],"125":[84],"\u00b0C":[85],"(\u2248":[86],"98%":[87],"accuracy)":[88],"without":[89],"the":[90,112],"use":[91],"ECC":[93],"significant":[95],"energy":[96,118],"saving":[97],"14%":[100],"QCNN":[102],"11%":[105],"QMLP.":[107],"A":[108],"detailed":[109],"analysis":[110],"tradeoff":[113],"between":[114],"retention":[115],"time,":[116],"write":[117],"is":[122],"presented.":[123]},"counts_by_year":[{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}