{"id":"https://openalex.org/W1998305460","doi":"https://doi.org/10.1145/2744769.2744904","title":"Scalable-effort classifiers for energy-efficient machine learning","display_name":"Scalable-effort classifiers for energy-efficient machine learning","publication_year":2015,"publication_date":"2015-06-02","ids":{"openalex":"https://openalex.org/W1998305460","doi":"https://doi.org/10.1145/2744769.2744904","mag":"1998305460"},"language":"en","primary_location":{"id":"doi:10.1145/2744769.2744904","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2744769.2744904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Design Automation Conference","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/A5010094713","display_name":"Swagath Venkataramani","orcid":"https://orcid.org/0000-0002-0470-6364"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Swagath Venkataramani","raw_affiliation_strings":["Purdue University, West Lafayette, IN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065766721","display_name":"Anand Raghunathan","orcid":"https://orcid.org/0000-0002-4624-564X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Raghunathan","raw_affiliation_strings":["Purdue University, West Lafayette, IN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454188","display_name":"Jie Liu","orcid":"https://orcid.org/0000-0003-1534-3500"},"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":"Jie Liu","raw_affiliation_strings":["Microsoft, One Microsoft Way, Redmond WA","[Microsoft, One Microsoft Way, Redmond, WA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, One Microsoft Way, Redmond WA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"[Microsoft, One Microsoft Way, Redmond, WA]","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100535963","display_name":"Mohammed Shoaib","orcid":null},"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":"Mohammed Shoaib","raw_affiliation_strings":["Microsoft, One Microsoft Way, Redmond WA","[Microsoft, One Microsoft Way, Redmond, WA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, One Microsoft Way, Redmond WA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"[Microsoft, One Microsoft Way, Redmond, WA]","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":109,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9945999979972839,"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":0.9945999979972839,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9890000224113464,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.8338900804519653},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.8173168897628784},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.8012341260910034},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7454218864440918},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6769307255744934},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.5376421809196472},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.49295902252197266},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.45251089334487915},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4495849609375},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1756446659564972},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10529637336730957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8338900804519653},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8173168897628784},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.8012341260910034},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7454218864440918},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6769307255744934},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.5376421809196472},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.49295902252197266},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.45251089334487915},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4495849609375},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1756446659564972},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10529637336730957},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2744769.2744904","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2744769.2744904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Design Automation Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W103892688","https://openalex.org/W237776227","https://openalex.org/W1485958089","https://openalex.org/W1597444092","https://openalex.org/W1926038792","https://openalex.org/W2007339694","https://openalex.org/W2025984714","https://openalex.org/W2028870622","https://openalex.org/W2057434193","https://openalex.org/W2060737402","https://openalex.org/W2071028614","https://openalex.org/W2089022734","https://openalex.org/W2101626661","https://openalex.org/W2102734279","https://openalex.org/W2105827278","https://openalex.org/W2112216367","https://openalex.org/W2119299853","https://openalex.org/W2126003678","https://openalex.org/W2128466927","https://openalex.org/W2133990480","https://openalex.org/W2134090756","https://openalex.org/W2135502357","https://openalex.org/W2142883190","https://openalex.org/W2155753979","https://openalex.org/W2156425544","https://openalex.org/W2164598857","https://openalex.org/W2168020168","https://openalex.org/W2168429197","https://openalex.org/W2168793192","https://openalex.org/W2182396527","https://openalex.org/W2197835450","https://openalex.org/W2206582914","https://openalex.org/W2206666148","https://openalex.org/W2277601133","https://openalex.org/W2912573428","https://openalex.org/W3097096317","https://openalex.org/W3120740533","https://openalex.org/W3214102110"],"related_works":["https://openalex.org/W1964832275","https://openalex.org/W2389865566","https://openalex.org/W2407804800","https://openalex.org/W2388864896","https://openalex.org/W2888937984","https://openalex.org/W2031651184","https://openalex.org/W2031173026","https://openalex.org/W2117265009","https://openalex.org/W2913388591","https://openalex.org/W2128012032"],"abstract_inverted_index":{"Supervised":[0],"machine-learning":[1,47],"algorithms":[2,94],"are":[3,142],"used":[4,168,208],"to":[5,19,40,191,209,227,267],"solve":[6],"classification":[7,54,93,212,231,249],"problems":[8],"across":[9,58],"the":[10,42,52,68,72,77,80,111,126,129,134,164,177,203,217],"entire":[11],"spectrum":[12],"of":[13,45,67,76,102,113,128,137,148,153,166,182,200,241],"computing":[14],"platforms,":[15],"from":[16],"data":[17],"centers":[18],"wearable":[20],"devices,":[21],"and":[22,269,276],"place":[23],"significant":[24],"demand":[25],"on":[26,98,125],"their":[27,103,121],"computational":[28,74,122],"capabilities.":[29],"In":[30],"this":[31,107],"paper,":[32],"we":[33,109],"propose":[34],"scalable-effort":[35,114,235,239,255],"classifiers,":[36,115,184],"a":[37,64,146,171,193,234],"new":[38],"approach":[39],"optimizing":[41],"energy":[43,273],"efficiency":[44],"supervised":[46],"classifiers.":[48],"We":[49,237],"observe":[50],"that":[51,118,254],"inherent":[53],"difficulty":[55,127],"varies":[56],"widely":[57],"inputs":[59,69],"in":[60,176,260,272],"real-world":[61],"datasets;":[62],"only":[63],"small":[65],"fraction":[66],"truly":[70],"require":[71],"full":[73],"effort":[75,97,123,140,158],"classifier,":[78],"while":[79,132],"large":[81],"majority":[82],"can":[83,213],"be":[84,214],"classified":[85],"correctly":[86],"with":[87,150],"very":[88],"low":[89],"effort.":[90],"Yet,":[91],"state-of-the-art":[92],"expend":[95],"equal":[96],"all":[99],"inputs,":[100],"irrespective":[101],"difficulty.":[104],"To":[105],"address":[106],"inefficiency,":[108],"introduce":[110],"concept":[112],"or":[116,220],"classifiers":[117,141,149,256],"dynamically":[119],"adjust":[120],"depending":[124],"input":[130],"data,":[131],"maintaining":[133],"same":[135],"level":[136],"accuracy.":[138],"Scalable":[139,157],"constructed":[143],"by":[144,162],"utilizing":[145],"chain":[147,178],"increasing":[151],"levels":[152],"complexity":[154],"(and":[155],"accuracy).":[156],"execution":[159],"is":[160,189,207],"achieved":[161],"modulating":[163],"number":[165],"stages":[167],"for":[169,274],"classifying":[170],"given":[172,230],"input.":[173],"Every":[174],"stage":[175,219],"contains":[179],"an":[180],"ensemble":[181],"biased":[183,187,204],"where":[185],"each":[186],"classifier":[188],"trained":[190],"detect":[192],"single":[194],"class":[195],"more":[196],"accurately.":[197],"The":[198],"degree":[199],"consensus":[201],"between":[202],"classifiers'":[205],"outputs":[206],"decide":[210],"whether":[211],"terminated":[215],"at":[216],"current":[218],"not.":[221],"Our":[222,251],"methodology":[223],"thus":[224],"allows":[225],"us":[226],"transform":[228],"any":[229],"algorithm":[232],"into":[233],"chain.":[236],"build":[238],"versions":[240],"8":[242],"popular":[243],"recognition":[244],"applications":[245],"using":[246],"3":[247],"different":[248],"algorithms.":[250],"experiments":[252],"demonstrate":[253],"yield":[257],"2.79x":[258],"reduction":[259],"average":[261],"operations":[262],"per":[263],"input,":[264],"which":[265],"translates":[266],"2.3x":[268],"1.5x":[270],"improvement":[271],"hardware":[275],"software":[277],"implementations,":[278],"respectively.":[279]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":5}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}