{"id":"https://openalex.org/W2727301630","doi":"https://doi.org/10.1145/3109761.3109804","title":"ADaPT","display_name":"ADaPT","publication_year":2017,"publication_date":"2017-10-17","ids":{"openalex":"https://openalex.org/W2727301630","doi":"https://doi.org/10.1145/3109761.3109804","mag":"2727301630"},"language":"en","primary_location":{"id":"doi:10.1145/3109761.3109804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109761.3109804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Internet of Things and Machine Learning","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/A5016261879","display_name":"Partha Maji","orcid":"https://orcid.org/0000-0002-1919-1228"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Partha Maji","raw_affiliation_strings":["University of Cambridge, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046649739","display_name":"Daniel Bates","orcid":"https://orcid.org/0000-0002-9526-3342"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniel Bates","raw_affiliation_strings":["University of Cambridge, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078055522","display_name":"Alex Chadwick","orcid":null},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alex Chadwick","raw_affiliation_strings":["University of Cambridge, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040762162","display_name":"Robert Mullins","orcid":"https://orcid.org/0000-0002-8393-2748"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Robert Mullins","raw_affiliation_strings":["University of Cambridge, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I241749"],"apc_list":null,"apc_paid":null,"fwci":0.7921,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.62711864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.5344134569168091}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5344134569168091}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3109761.3109804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3109761.3109804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Conference on Internet of Things and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W336063547","https://openalex.org/W1841592590","https://openalex.org/W1998399571","https://openalex.org/W1999085092","https://openalex.org/W2040376026","https://openalex.org/W2114766824","https://openalex.org/W2119144962","https://openalex.org/W2125389748","https://openalex.org/W2131524184","https://openalex.org/W2132267493","https://openalex.org/W2150947797","https://openalex.org/W2177847924","https://openalex.org/W2181101938","https://openalex.org/W2260663238","https://openalex.org/W2300805302","https://openalex.org/W2337344472","https://openalex.org/W2513928851","https://openalex.org/W2525098248","https://openalex.org/W2553910756","https://openalex.org/W2557283755","https://openalex.org/W2580425382","https://openalex.org/W2743095234","https://openalex.org/W2906043559","https://openalex.org/W2949245006","https://openalex.org/W2951770173","https://openalex.org/W2952054889","https://openalex.org/W3022861191","https://openalex.org/W4249142012"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2093578348","https://openalex.org/W2376932109","https://openalex.org/W2382290278","https://openalex.org/W2766271392","https://openalex.org/W2350741829","https://openalex.org/W3004735627"],"abstract_inverted_index":{"Breakthroughs":[0],"from":[1,155,209],"the":[2,40,58,97,123],"field":[3],"of":[4,42,60,122],"deep":[5,170],"learning":[6],"are":[7,13,38],"radically":[8],"changing":[9],"how":[10],"sensor":[11],"data":[12],"interpreted":[14],"to":[15,19,85,100,118,132,158],"extract":[16],"important":[17],"information":[18],"help":[20],"advance":[21],"healthcare,":[22],"make":[23,119],"our":[24,190],"cities":[25],"smarter,":[26],"and":[27,53,103,148,172,204],"innovate":[28],"in":[29,51,64,96,168,215],"smart":[30],"home":[31],"technology.":[32],"Deep":[33],"convolutional":[34,65,98,180,210],"neural":[35],"networks,":[36],"which":[37,81],"at":[39,57],"heart":[41],"many":[43,178],"emerging":[44],"Internet-of-Things":[45],"(IoT)":[46],"applications,":[47],"achieve":[48],"remarkable":[49],"performance":[50],"audio":[52],"visual":[54],"recognition":[55],"tasks,":[56],"expense":[59],"high":[61],"computational":[62],"complexity":[63],"layers,":[66],"limiting":[67],"their":[68],"deployability.":[69],"In":[70],"this":[71,143],"paper,":[72],"we":[73,107],"present":[74,95],"an":[75,164],"easy-to-implement":[76],"acceleration":[77],"scheme,":[78],"named":[79],"ADaPT,":[80],"can":[82,128],"be":[83,130],"applied":[84,131],"already":[86],"available":[87],"pre-trained":[88],"networks.":[89],"Our":[90,161],"proposed":[91],"technique":[92,127,144,192],"exploits":[93],"redundancy":[94],"layers":[99,211],"reduce":[101],"computation":[102],"storage":[104],"requirements.":[105],"Additionally,":[106],"also":[108],"decompose":[109],"each":[110],"convolution":[111],"layer":[112],"into":[113],"two":[114],"consecutive":[115],"one-dimensional":[116],"stages":[117],"full":[120],"use":[121],"approximate":[124],"model.":[125],"This":[126],"easily":[129],"existing":[133],"low":[134],"power":[135],"processors,":[136],"GPUs":[137],"or":[138],"new":[139],"accelerators.":[140],"We":[141,182],"evaluated":[142],"using":[145],"four":[146],"diverse":[147],"widely":[149],"used":[150],"benchmarks,":[151],"on":[152,177],"hardware":[153],"ranging":[154],"embedded":[156],"CPUs":[157],"server":[159],"GPUs.":[160],"experiments":[162],"show":[163],"average":[165],"3-5x":[166],"speed-up":[167,176],"all":[169],"models":[171],"a":[173],"maximum":[174],"8-9x":[175],"individual":[179],"layers.":[181],"demonstrate":[183],"that":[184],"unlike":[185],"iterative":[186],"pruning":[187],"based":[188],"methodology,":[189],"approximation":[191],"is":[193],"mathematically":[194],"well":[195],"grounded,":[196],"robust,":[197],"does":[198],"not":[199],"require":[200],"any":[201],"time-consuming":[202],"retraining,":[203],"still":[205],"achieves":[206],"speed-ups":[207],"solely":[208],"with":[212],"no":[213],"loss":[214],"baseline":[216],"accuracy.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2017-07-14T00:00:00"}