{"id":"https://openalex.org/W4223565328","doi":"https://doi.org/10.1109/iceic54506.2022.9748777","title":"A Gradient-Descent Calibration Method to Mitigate Process Variations in Analog Synapse Arrays","display_name":"A Gradient-Descent Calibration Method to Mitigate Process Variations in Analog Synapse Arrays","publication_year":2022,"publication_date":"2022-02-06","ids":{"openalex":"https://openalex.org/W4223565328","doi":"https://doi.org/10.1109/iceic54506.2022.9748777"},"language":"en","primary_location":{"id":"doi:10.1109/iceic54506.2022.9748777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic54506.2022.9748777","pdf_url":null,"source":{"id":"https://openalex.org/S4363608213","display_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","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/A5059067053","display_name":"Seung\u2010heon Chris Baek","orcid":"https://orcid.org/0000-0003-1338-8238"},"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":"Seung-Heon Baek","raw_affiliation_strings":["Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea","Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015393579","display_name":"Jaeha Kim","orcid":"https://orcid.org/0000-0003-2237-3134"},"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":"Jaeha Kim","raw_affiliation_strings":["Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea","Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059067053"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0173454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10320","display_name":"Neural Networks and Applications","score":0.9994000196456909,"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/T10320","display_name":"Neural Networks and Applications","score":0.9994000196456909,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9980000257492065,"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":0.7952660322189331},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6764304041862488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6730313301086426},{"id":"https://openalex.org/keywords/process-variation","display_name":"Process variation","score":0.6714015603065491},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.665464460849762},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6530646681785583},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5209506750106812},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.49507561326026917},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4463229179382324},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.435573011636734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40213096141815186},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30312949419021606},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15284323692321777}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7952660322189331},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6764304041862488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6730313301086426},{"id":"https://openalex.org/C93389723","wikidata":"https://www.wikidata.org/wiki/Q7247313","display_name":"Process variation","level":3,"score":0.6714015603065491},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.665464460849762},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6530646681785583},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5209506750106812},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.49507561326026917},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4463229179382324},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.435573011636734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40213096141815186},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30312949419021606},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15284323692321777},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/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/iceic54506.2022.9748777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic54506.2022.9748777","pdf_url":null,"source":{"id":"https://openalex.org/S4363608213","display_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2523246573","https://openalex.org/W2809230488","https://openalex.org/W2912910336","https://openalex.org/W2966285227","https://openalex.org/W2981240739","https://openalex.org/W4251370906","https://openalex.org/W6727249380"],"related_works":["https://openalex.org/W4315785049","https://openalex.org/W3015792155","https://openalex.org/W3118286710","https://openalex.org/W2137012493","https://openalex.org/W2937906318","https://openalex.org/W4225929918","https://openalex.org/W3095450733","https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654"],"abstract_inverted_index":{"A":[0],"technique":[1,19,73,89],"to":[2,93,100,103],"compensate":[3],"the":[4,21,26,39,48,51,57,63,66,71,88,91,94],"effects":[5,52],"of":[6,28,53],"process":[7,22,54,104],"variation":[8,23,55],"in":[9,65],"analog-based":[10],"neural":[11,30,58],"computation":[12],"circuit":[13,40],"arrays":[14],"is":[15,74],"presented.":[16],"The":[17],"proposed":[18,72],"considers":[20],"that":[24],"degrades":[25],"accuracy":[27,68,92],"a":[29,33,43,77],"network":[31,59],"as":[32],"variable":[34],"for":[35,83],"optimization":[36],"and":[37,62],"adjusts":[38],"parameters":[41],"via":[42],"gradient":[44],"descent":[45],"method.":[46],"In":[47],"TensorFlow":[49],"framework,":[50],"on":[56],"are":[60],"modeled":[61],"improvement":[64],"inference":[67],"achieved":[69],"by":[70],"analyzed.":[75],"For":[76],"4-layer":[78],"CNN":[79],"MNIST":[80],"example":[81],"driven":[82],"1000":[84],"different":[85],"variations":[86],"sets,":[87],"restores":[90],"original":[95],"level,":[96],"which":[97],"can":[98],"degrade":[99],"17.3~95.2%":[101],"due":[102],"variations.":[105]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}