{"id":"https://openalex.org/W4205124414","doi":"https://doi.org/10.1109/tcad.2022.3144616","title":"AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency","display_name":"AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency","publication_year":2022,"publication_date":"2022-01-19","ids":{"openalex":"https://openalex.org/W4205124414","doi":"https://doi.org/10.1109/tcad.2022.3144616"},"language":"en","primary_location":{"id":"doi:10.1109/tcad.2022.3144616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcad.2022.3144616","pdf_url":null,"source":{"id":"https://openalex.org/S100835903","display_name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","issn_l":"0278-0070","issn":["0278-0070","1937-4151"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","raw_type":"journal-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/A5103085687","display_name":"Fuxun Yu","orcid":"https://orcid.org/0000-0002-4880-6658"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fuxun Yu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030027584","display_name":"Zirui Xu","orcid":"https://orcid.org/0000-0002-3556-9358"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zirui Xu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767202","display_name":"Chenchen Liu","orcid":"https://orcid.org/0000-0001-7749-0640"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Liu","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032077042","display_name":"Dimitrios Stamoulis","orcid":"https://orcid.org/0000-0003-1682-9350"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitrios Stamoulis","raw_affiliation_strings":["Microsoft Cognition, Microsoft, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Cognition, Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102729322","display_name":"Di Wang","orcid":"https://orcid.org/0000-0001-8003-9738"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["Microsoft Cognition, Microsoft, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Cognition, Microsoft, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100651384","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0000-0002-3024-7990"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100441957","display_name":"Xiang Chen","orcid":"https://orcid.org/0000-0003-2790-976X"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Chen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5103085687"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.3057,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51846013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"41","issue":"11","first_page":"4694","last_page":"4707"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.9987000226974487,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.998199999332428,"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/computer-science","display_name":"Computer science","score":0.7452396154403687},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5656706094741821},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5518942475318909},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.536119818687439},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4851706624031067},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4731498956680298},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4595133364200592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4457133114337921},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.4396193027496338}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7452396154403687},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5656706094741821},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5518942475318909},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.536119818687439},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4851706624031067},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4731498956680298},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4595133364200592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4457133114337921},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.4396193027496338},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcad.2022.3144616","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcad.2022.3144616","pdf_url":null,"source":{"id":"https://openalex.org/S100835903","display_name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","issn_l":"0278-0070","issn":["0278-0070","1937-4151"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6676950412","display_name":null,"funder_award_id":"CNS-2003211","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G789971151","display_name":null,"funder_award_id":"CNS-1939380","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2143612262","https://openalex.org/W2559655401","https://openalex.org/W2752782242","https://openalex.org/W2766839578","https://openalex.org/W2792641098","https://openalex.org/W2884585870","https://openalex.org/W2886851211","https://openalex.org/W2889185260","https://openalex.org/W2896006880","https://openalex.org/W2896457183","https://openalex.org/W2900423325","https://openalex.org/W2928560789","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2963094099","https://openalex.org/W2963150697","https://openalex.org/W2963263347","https://openalex.org/W2963363373","https://openalex.org/W2963674932","https://openalex.org/W2964233199","https://openalex.org/W2981698279","https://openalex.org/W3000207760","https://openalex.org/W3012561096","https://openalex.org/W3013407975","https://openalex.org/W3028148258","https://openalex.org/W3035467254","https://openalex.org/W3035678286","https://openalex.org/W3092382944","https://openalex.org/W3130473750","https://openalex.org/W3133641570","https://openalex.org/W4213084750","https://openalex.org/W4236853429","https://openalex.org/W4292779060","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6638632666","https://openalex.org/W6638667902","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6726497184","https://openalex.org/W6739901393","https://openalex.org/W6739917289","https://openalex.org/W6743912273","https://openalex.org/W6745499037","https://openalex.org/W6755034786","https://openalex.org/W6755207826","https://openalex.org/W6755526309","https://openalex.org/W6755904014","https://openalex.org/W6757036269","https://openalex.org/W6762357854","https://openalex.org/W6762718338","https://openalex.org/W6767064347","https://openalex.org/W6768372512","https://openalex.org/W6778883912","https://openalex.org/W6784784916","https://openalex.org/W6791281132"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W2061300913","https://openalex.org/W4306674287","https://openalex.org/W4312263439","https://openalex.org/W1538624230","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W1629725936","https://openalex.org/W3199608561"],"abstract_inverted_index":{"Deep":[0],"neural":[1,107],"networks":[2],"(DNNs)":[3],"achieved":[4],"great":[5],"cognitive":[6],"performance":[7],"at":[8,272],"the":[9,18,28,68,73,82,106,155,171,176,181,185,191,203,260],"expense":[10],"of":[11],"a":[12,96,113,143,196],"considerable":[13],"computation":[14],"workload.":[15],"To":[16],"relieve":[17],"computational":[19],"burden,":[20],"many":[21],"optimization":[22,99,116,127,263],"works":[23,48],"are":[24],"developed":[25],"to":[26,67,211,224,266,270],"reduce":[27],"model":[29,36,51,74,138,192,210,219,269],"redundancy":[30,168],"by":[31,128,169],"identifying":[32],"and":[33,42,133,148,163,174,188,233],"removing":[34],"insignificant":[35],"components,":[37],"such":[38],"as":[39],"weight":[40],"sparsity":[41,215],"filter":[43],"pruning":[44,178,187,200],"methods.":[45],"However,":[46],"these":[47],"only":[49,86],"evaluate":[50],"components\u2019":[52,75],"static":[53,83],"significance":[54,76],"with":[55,62,130,195,251],"parameter":[56],"information,":[57],"ignoring":[58],"their":[59],"dynamic":[60,97,115,121,145,186],"interaction":[61,173],"external":[63],"inputs.":[64],"Specifically,":[65],"due":[66],"difference":[69],"in":[70,101],"per-input":[71,166],"features,":[72],"can":[77,85,160,220],"dynamically":[78,222],"change":[79],"and,":[80],"thus,":[81],"methods":[84],"achieve":[87],"suboptimal":[88],"performance.":[89],"Focusing":[90],"on":[91,105,255],"this":[92,102],"aspect,":[93],"we":[94,111],"propose":[95,112],"DNN":[98],"framework":[100],"work.":[103],"Based":[104],"network":[108],"attention":[109],"mechanism,":[110],"comprehensive":[114],"framework,":[117,151],"including":[118],"1)":[119],"testing-phase":[120],"feature":[122,167,199,214],"map":[123],"pruning;":[124],"2)":[125],"training-phase":[126],"training":[129],"targeted":[131],"dropout;":[132],"3)":[134],"deployment-phase":[135],"one-for-all":[136],"(OFA)":[137],"adaptability":[139],"enhancement.":[140],"By":[141],"providing":[142],"holistic":[144],"testing,":[146],"training,":[147],"deployment":[149,204,237,262],"co-optimization":[150,183],"our":[152,243],"work":[153],"has":[154],"following":[156],"benefits:":[157],"first,":[158],"it":[159],"accurately":[161],"identify":[162],"aggressively":[164],"remove":[165],"considering":[170],"model-input":[172],"involving":[175],"channel/column-wise":[177],"flexibility;":[179],"meanwhile,":[180],"training-testing":[182],"favors":[184],"helps":[189],"maintain":[190],"accuracy":[193,253],"even":[194],"very":[197],"high":[198],"ratio.":[201],"Finally,":[202],"enhancement":[205],"provides":[206],"one":[207,268],"unified":[208,218],"OFA":[209,261],"support":[212,271],"full-spectrum":[213],"ratios.":[216],"The":[217],"be":[221],"reconfigured":[223],"meet":[225],"different":[226,275],"resource":[227,276],"budgets":[228],"without":[229,278],"any":[230,279],"retraining":[231,280],"cost,":[232],"thus":[234],"provide":[235],"significant":[236],"flexibility.":[238],"Extensive":[239],"experiments":[240],"show":[241],"that":[242],"method":[244],"could":[245],"bring":[246],"37.4%\u201354.5%":[247],"floating-point":[248],"operations":[249],"reduction":[250],"negligible":[252],"drop":[254],"various":[256],"test":[257],"benchmarks.":[258],"Meanwhile,":[259],"enables":[264],"us":[265],"use":[267],"most":[273],"ten":[274],"constraints":[277],"cost.":[281]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
