{"id":"https://openalex.org/W3162617397","doi":"https://doi.org/10.1109/cicc51472.2021.9431398","title":"An In-Memory-Computing Charge-Domain Ternary CNN Classifier","display_name":"An In-Memory-Computing Charge-Domain Ternary CNN Classifier","publication_year":2021,"publication_date":"2021-04-01","ids":{"openalex":"https://openalex.org/W3162617397","doi":"https://doi.org/10.1109/cicc51472.2021.9431398","mag":"3162617397"},"language":"en","primary_location":{"id":"doi:10.1109/cicc51472.2021.9431398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicc51472.2021.9431398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Custom Integrated Circuits Conference (CICC)","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/A5051423351","display_name":"Xiangxing Yang","orcid":"https://orcid.org/0000-0001-8712-2884"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiangxing Yang","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079056437","display_name":"Keren Zhu","orcid":"https://orcid.org/0000-0003-2698-141X"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keren Zhu","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066653944","display_name":"Xiyuan Tang","orcid":"https://orcid.org/0000-0003-2181-9042"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiyuan Tang","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067606265","display_name":"Meizhi Wang","orcid":"https://orcid.org/0000-0001-7029-5244"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meizhi Wang","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027635951","display_name":"Mingtao Zhan","orcid":"https://orcid.org/0009-0009-5767-179X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingtao Zhan","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039650266","display_name":"Nanshu Lu","orcid":"https://orcid.org/0000-0002-3595-3851"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nanshu Lu","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003048953","display_name":"Jaydeep P. Kulkarni","orcid":"https://orcid.org/0000-0002-0258-6776"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaydeep P. Kulkarni","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011883763","display_name":"David Z. Pan","orcid":"https://orcid.org/0000-0002-5705-2501"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Z. Pan","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045721867","display_name":"Yongpan Liu","orcid":"https://orcid.org/0000-0002-4892-2309"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongpan Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070329670","display_name":"Nan Sun","orcid":"https://orcid.org/0000-0002-5536-8385"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Nan Sun","raw_affiliation_strings":["Tsinghua University, Beijing, China","University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5051423351"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":1.4038,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.80908472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"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":0.9998999834060669,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9927999973297119,"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":0.9512888193130493},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7523030638694763},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7156953811645508},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5035943388938904},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4959934651851654},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.4530550539493561},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.44732093811035156},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4136897325515747},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41227686405181885},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36639201641082764},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10273650288581848}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9512888193130493},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7523030638694763},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7156953811645508},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5035943388938904},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4959934651851654},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4530550539493561},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.44732093811035156},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4136897325515747},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41227686405181885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36639201641082764},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10273650288581848},{"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},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cicc51472.2021.9431398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicc51472.2021.9431398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Custom Integrated Circuits Conference (CICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2777372517","https://openalex.org/W2793168176","https://openalex.org/W2920326572","https://openalex.org/W3017968097","https://openalex.org/W3043783230"],"related_works":["https://openalex.org/W3036048022","https://openalex.org/W3156786002","https://openalex.org/W2529764055","https://openalex.org/W4280607397","https://openalex.org/W4306820094","https://openalex.org/W3138828421","https://openalex.org/W4372272185","https://openalex.org/W3168594759","https://openalex.org/W3165298724","https://openalex.org/W2943664440"],"abstract_inverted_index":{"Al":[0],"edge":[1],"devices":[2],"require":[3],"local":[4],"intelligence":[5],"for":[6,85,115,158,224],"the":[7,14,29,77,143,159,178,183,188,207,220],"concerns":[8],"of":[9,37],"latency":[10],"and":[11,16,34,56,63,79,88],"privacy.":[12],"Given":[13],"accuracy":[15,213],"energy":[17,33,92,133],"constraints,":[18],"low-power":[19],"convolutional":[20],"neural":[21,69,203],"networks":[22],"(CNNs)":[23],"are":[24],"gaining":[25],"popularity.":[26],"To":[27,119],"alleviate":[28],"high":[30],"memory":[31],"access":[32],"computational":[35],"cost":[36],"large":[38],"CNN":[39,128],"models,":[40],"prior":[41],"works":[42],"have":[43],"proposed":[44,144,208],"promising":[45],"approaches":[46],"including":[47],"in-memory-computing":[48],"(IMC)":[49],"[1],":[50],"mixed-signal":[51,126],"multiply-and-accumulate":[52],"(MAC)":[53],"calculation":[54],"[2],":[55],"reduced":[57,179],"resolution":[58,103,149],"network":[59,70,146,204],"[3]-[4].":[60],"With":[61,200],"weights":[62],"activations":[64],"restricted":[65],"to":[66,101,152],"\u00b11,":[67],"binary":[68],"(BNN)":[71],"combining":[72],"with":[73,214],"'MC":[74],"greatly":[75],"improves":[76],"storage":[78],"computation":[80],"efficiency,":[81],"making":[82],"it":[83],"wellsuited":[84],"edge-based":[86],"applications,":[87],"has":[89],"demonstrated":[90],"state-ofthe-art":[91],"efficiency":[93,134,223],"in":[94,196],"image":[95],"classification":[96],"problems":[97],"[5].":[98],"However,":[99],"compared":[100],"full":[102],"network,":[104],"BNN":[105,157],"requires":[106],"larger":[107],"model":[108],"thus":[109],"more":[110],"operations":[111],"(OPs)":[112],"per":[113,217],"inference":[114],"a":[116,125,164,201],"certain":[117],"accuracy.":[118,227],"address":[120],"such":[121],"challenge,":[122],"we":[123],"propose":[124],"ternary":[127,145],"based":[129],"processor":[130],"featuring":[131],"higher":[132],"than":[135,156],"BNN.":[136],"It":[137],"confers":[138],"several":[139],"key":[140],"improvements:":[141],"1)":[142],"provides":[147],"1.5-b":[148],"(01+1/-1),":[150],"leading":[151],"3.9x":[153],"OPs/inference":[154],"reduction":[155],"same":[160],"MNIST":[161,212,226],"accuracy;":[162],"2)":[163],"1.5b":[165],"MAC":[166,190],"is":[167],"implemented":[168],"by":[169],"VcM-based":[170,189],"capacitor":[171],"switching":[172,198],"scheme,":[173],"which":[174],"inherently":[175],"benefits":[176],"from":[177],"signal":[180],"swing":[181],"on":[182,205],"capacitive":[184],"DAC":[185],"(CDAC);":[186],"3)":[187],"introduces":[191],"sparsity":[192],"during":[193],"training,":[194],"resulting":[195],"lower":[197],"rate.":[199],"complete":[202],"chip,":[206],"design":[209],"realizes":[210],"97.1%":[211],"only":[215],"0.18uJ":[216],"classification,":[218],"presenting":[219],"highest":[221],"power":[222],"comparable":[225]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
