{"id":"https://openalex.org/W4312857492","doi":"https://doi.org/10.1109/saci55618.2022.9919465","title":"Benchmarking TensorFlow Lite Quantization Algorithms for Deep Neural Networks","display_name":"Benchmarking TensorFlow Lite Quantization Algorithms for Deep Neural Networks","publication_year":2022,"publication_date":"2022-05-25","ids":{"openalex":"https://openalex.org/W4312857492","doi":"https://doi.org/10.1109/saci55618.2022.9919465"},"language":"en","primary_location":{"id":"doi:10.1109/saci55618.2022.9919465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saci55618.2022.9919465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5084795605","display_name":"Ioan Lucan Or\u0103\u0219an","orcid":"https://orcid.org/0000-0001-7628-3260"},"institutions":[{"id":"https://openalex.org/I3122695212","display_name":"Polytechnic University of Timi\u015foara","ror":"https://ror.org/02v91gy68","country_code":"RO","type":"education","lineage":["https://openalex.org/I3122695212"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Ioan Lucan Orasan","raw_affiliation_strings":["Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","institution_ids":["https://openalex.org/I3122695212"]},{"raw_affiliation_string":"Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania","institution_ids":["https://openalex.org/I3122695212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015409850","display_name":"Ciprian Seiculescu","orcid":null},"institutions":[{"id":"https://openalex.org/I3122695212","display_name":"Polytechnic University of Timi\u015foara","ror":"https://ror.org/02v91gy68","country_code":"RO","type":"education","lineage":["https://openalex.org/I3122695212"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Ciprian Seiculescu","raw_affiliation_strings":["Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","institution_ids":["https://openalex.org/I3122695212"]},{"raw_affiliation_string":"Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania","institution_ids":["https://openalex.org/I3122695212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037751847","display_name":"C\u0103t\u0103lin Daniel C\u0103leanu","orcid":"https://orcid.org/0000-0002-0252-7047"},"institutions":[{"id":"https://openalex.org/I3122695212","display_name":"Polytechnic University of Timi\u015foara","ror":"https://ror.org/02v91gy68","country_code":"RO","type":"education","lineage":["https://openalex.org/I3122695212"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Catalin Daniel Caleanu","raw_affiliation_strings":["Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Politehnica University Timisoara,Faculty of Electronics, Telecommunications and Information Technologies,Departament of Applied Electronics,Timisoara,Romania","institution_ids":["https://openalex.org/I3122695212"]},{"raw_affiliation_string":"Departament of Applied Electronics, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University Timisoara, Timisoara, Romania","institution_ids":["https://openalex.org/I3122695212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3122695212"],"apc_list":null,"apc_paid":null,"fwci":1.1914,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.87012209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"000221","last_page":"000226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9919999837875366,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9919999837875366,"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/T10320","display_name":"Neural Networks and Applications","score":0.986299991607666,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9567999839782715,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.8767708539962769},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7081291675567627},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6849241852760315},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.681580662727356},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6532491445541382},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6181420087814331},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.580328106880188},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5210953950881958},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5066280961036682},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49921655654907227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48723259568214417},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4765204191207886}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8767708539962769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7081291675567627},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6849241852760315},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.681580662727356},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6532491445541382},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6181420087814331},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.580328106880188},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5210953950881958},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5066280961036682},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49921655654907227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48723259568214417},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4765204191207886},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/saci55618.2022.9919465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saci55618.2022.9919465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2564111483","https://openalex.org/W2752782242","https://openalex.org/W2809624076","https://openalex.org/W2888259257","https://openalex.org/W2963122961","https://openalex.org/W2963446712","https://openalex.org/W3023935494","https://openalex.org/W3084623993","https://openalex.org/W3093515987","https://openalex.org/W3185492777","https://openalex.org/W4200370950","https://openalex.org/W4206411449","https://openalex.org/W4287639545","https://openalex.org/W4287828539","https://openalex.org/W4405633122","https://openalex.org/W6638523607","https://openalex.org/W6753069482","https://openalex.org/W6774302960","https://openalex.org/W6784225549","https://openalex.org/W6787948820"],"related_works":["https://openalex.org/W3098379356","https://openalex.org/W2962391030","https://openalex.org/W1537716277","https://openalex.org/W3205916482","https://openalex.org/W2806031239","https://openalex.org/W4312857492","https://openalex.org/W3000364559","https://openalex.org/W2994782216","https://openalex.org/W3186377950","https://openalex.org/W3158216587"],"abstract_inverted_index":{"Deploying":[0],"deep":[1,63,86,109],"neural":[2,87,110],"network":[3],"models":[4,88,111],"on":[5,143],"the":[6,32,38,96,100,122,127,135,139,144],"resource":[7],"constrained":[8],"devices,":[9],"e.g.,":[10],"lost-cost":[11],"microcontrollers,":[12],"is":[13,29,78],"challenging":[14],"because":[15],"they":[16],"are":[17],"mostly":[18],"limited":[19],"in":[20],"terms":[21],"of":[22,31,40,56,75,89,95,112,121],"memory":[23],"footprint":[24],"and":[25,115,130],"computation":[26],"capabilities.":[27],"Quantization":[28],"one":[30],"widely":[33],"used":[34],"solutions":[35],"to":[36,79,126,137],"reduce":[37],"size":[39],"a":[41,119,132],"model.":[42],"For":[43],"parameter":[44],"representation,":[45],"it":[46],"employs":[47],"for":[48,70,104,108,134],"example":[49],"just":[50],"8-bit":[51],"integer":[52],"or":[53],"less":[54],"instead":[55],"32-bit":[57],"floating":[58],"point.":[59],"The":[60,73,92],"TensorFlow":[61],"Lite":[62],"learning":[64],"framework":[65],"currently":[66],"provides":[67],"four":[68],"methods":[69,83],"post-training":[71],"quantization.":[72],"aim":[74],"this":[76],"paper":[77,97],"benchmark":[80],"these":[81],"quantization":[82,106,140],"using":[84],"various":[85],"different":[90],"sizes.":[91],"main":[93],"outcomes":[94],"are:":[98],"(1)":[99],"compression":[101],"ratio":[102],"obtained":[103],"each":[105],"method":[107,141],"small,":[113],"medium,":[114],"large":[116],"sizes,":[117],"(2)":[118],"comparison":[120],"accuracy":[123],"results":[124],"relative":[125],"original":[128],"accuracy,":[129],"(3)":[131],"viewpoint":[133],"decision":[136],"choose":[138],"depending":[142],"model":[145],"size.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-19T15:00:24.278416","created_date":"2025-10-10T00:00:00"}