{"id":"https://openalex.org/W4408863098","doi":"https://doi.org/10.1109/icce63647.2025.10930053","title":"Hardware-Friendly Quantization via Outlier Scaling in Convolution-Attention-Based Hybrid Networks","display_name":"Hardware-Friendly Quantization via Outlier Scaling in Convolution-Attention-Based Hybrid Networks","publication_year":2025,"publication_date":"2025-01-11","ids":{"openalex":"https://openalex.org/W4408863098","doi":"https://doi.org/10.1109/icce63647.2025.10930053"},"language":"en","primary_location":{"id":"doi:10.1109/icce63647.2025.10930053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce63647.2025.10930053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Consumer Electronics (ICCE)","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/A5110741547","display_name":"Nam Joon Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Nam Joon Kim","raw_affiliation_strings":["Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811"],"affiliations":[{"raw_affiliation_string":"Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100371185","display_name":"Hyun Kim","orcid":"https://orcid.org/0000-0002-7962-657X"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Kim","raw_affiliation_strings":["Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811"],"affiliations":[{"raw_affiliation_string":"Seoul National University of Science and Technology,Research Center for Electrical and Information Technology,Department of Electrical and Information Engineering,Seoul,Korea,01811","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110741547"],"corresponding_institution_ids":["https://openalex.org/I118373667"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02466073,"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":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9821000099182129,"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/T12676","display_name":"Machine Learning and ELM","score":0.9821000099182129,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9642999768257141,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.932200014591217,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"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/scaling","display_name":"Scaling","score":0.7591469287872314},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.7512942552566528},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7215734720230103},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6123092174530029},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5976707339286804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4057973027229309},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3658008873462677},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3435576558113098},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.325237512588501},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1863449513912201},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.11703228950500488}],"concepts":[{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.7591469287872314},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7512942552566528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215734720230103},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6123092174530029},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5976707339286804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4057973027229309},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3658008873462677},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3435576558113098},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.325237512588501},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1863449513912201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11703228950500488},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce63647.2025.10930053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce63647.2025.10930053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2963163009","https://openalex.org/W2998218113","https://openalex.org/W3138516171","https://openalex.org/W4285059684","https://openalex.org/W4285601701","https://openalex.org/W4312914306","https://openalex.org/W4389169770","https://openalex.org/W4393318691","https://openalex.org/W4393373882","https://openalex.org/W4400233889","https://openalex.org/W4400811646","https://openalex.org/W4401024916","https://openalex.org/W4401386645","https://openalex.org/W6802648153"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2510582230","https://openalex.org/W2978674666","https://openalex.org/W2074430941","https://openalex.org/W141820298","https://openalex.org/W2113096305","https://openalex.org/W1977636359","https://openalex.org/W2772305933"],"abstract_inverted_index":{"Hybrid":[0],"networks":[1,26,38,48],"that":[2,110],"combine":[3],"convolution":[4],"and":[5],"attention":[6],"have":[7,39],"achieved":[8],"state-of-the-art":[9],"performance":[10],"in":[11,27,72,81],"computer":[12],"vision":[13],"tasks.":[14],"Quantization":[15],"is":[16],"a":[17,61,97,135,149],"promising":[18],"compression":[19],"method":[20,101,109,142],"to":[21,46,86,159],"efficiently":[22],"utilize":[23],"these":[24,91],"hybrid":[25,37,47,77,137],"resource-constrained":[28],"consumer":[29],"electronics":[30],"like":[31],"IoT":[32],"devices.":[33],"However,":[34],"although":[35],"many":[36],"been":[40,52],"proposed,":[41],"research":[42],"on":[43,130],"quantization":[44,65,161],"dedicated":[45],"has":[49],"not":[50,95],"yet":[51],"actively":[53],"conducted.":[54],"To":[55,88,119],"bridge":[56],"this":[57],"gap,":[58],"we":[59,68,93],"propose":[60,94],"novel":[62,98],"hardware-friendly":[63,116],"post-training":[64],"method.":[66],"Initially,":[67],"observe":[69],"significant":[70],"outliers":[71],"the":[73,121,124,131,145,154],"bottleneck":[74],"blocks":[75],"of":[76,123],"networks,":[78],"which":[79],"result":[80],"severe":[82],"accuracy":[83,146],"degradation":[84],"due":[85],"quantization.":[87],"effectively":[89],"address":[90],"outliers,":[92],"only":[96],"outlier":[99],"scaling":[100],"but":[102],"also":[103],"an":[104],"objective":[105],"function-based":[106],"power-of-two":[107],"approximation":[108],"replaces":[111],"conventional":[112],"floating-point":[113],"multiplication":[114],"with":[115,148],"shift":[117],"operations.":[118],"demonstrate":[120],"effectiveness":[122],"proposed":[125,141],"method,":[126],"experiments":[127],"were":[128],"conducted":[129],"ImageNet-1k":[132],"dataset":[133],"using":[134],"representative":[136],"network,":[138],"MobileViT.":[139],"Our":[140],"significantly":[143],"mitigated":[144],"drop":[147],"small":[150],"parameter":[151],"increase":[152],"at":[153],"same":[155],"model":[156],"size":[157],"compared":[158],"existing":[160],"methods.":[162]},"counts_by_year":[],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
