{"id":"https://openalex.org/W2971428146","doi":"https://doi.org/10.1109/islped.2019.8824934","title":"TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks","display_name":"TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2971428146","doi":"https://doi.org/10.1109/islped.2019.8824934","mag":"2971428146"},"language":"en","primary_location":{"id":"doi:10.1109/islped.2019.8824934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped.2019.8824934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","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/A5101775553","display_name":"Lile Cai","orcid":"https://orcid.org/0000-0001-8783-0186"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lile Cai","raw_affiliation_strings":["I2R, Singapore"],"affiliations":[{"raw_affiliation_string":"I2R, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051898201","display_name":"Anne-Maelle Barneche","orcid":null},"institutions":[{"id":"https://openalex.org/I102475099","display_name":"Sup\u00e9lec","ror":"https://ror.org/00n7gwn90","country_code":"FR","type":"education","lineage":["https://openalex.org/I102475099"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Anne-Maelle Barneche","raw_affiliation_strings":["SUPELEC, France"],"affiliations":[{"raw_affiliation_string":"SUPELEC, France","institution_ids":["https://openalex.org/I102475099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078507284","display_name":"Arthur Herbout","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arthur Herbout","raw_affiliation_strings":["ECP, France"],"affiliations":[{"raw_affiliation_string":"ECP, France","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001103949","display_name":"Chuan-Sheng Foo","orcid":"https://orcid.org/0000-0002-4748-5792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuan Sheng Foo","raw_affiliation_strings":["I2R, Singapore"],"affiliations":[{"raw_affiliation_string":"I2R, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030005076","display_name":"Jie Lin","orcid":"https://orcid.org/0000-0002-8971-0660"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Lin","raw_affiliation_strings":["I2R, Singapore"],"affiliations":[{"raw_affiliation_string":"I2R, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103502228","display_name":"Vijay Chandrasekhar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijay Ramaseshan Chandrasekhar","raw_affiliation_strings":["I2R, Singapore"],"affiliations":[{"raw_affiliation_string":"I2R, Singapore","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011667950","display_name":"Mohamed M. Sabry Aly","orcid":"https://orcid.org/0000-0002-8018-1264"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed M. Sabry Aly","raw_affiliation_strings":["NTU, Singapore"],"affiliations":[{"raw_affiliation_string":"NTU, Singapore","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101775553"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5184,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.86473924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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":1.0,"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":1.0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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":0.9979000091552734,"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":0.7928159832954407},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7613444328308105},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6772432923316956},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.668748676776886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5855196714401245},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49797797203063965},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4760846197605133},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46171805262565613},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4588601589202881},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.4500456750392914},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.42763805389404297},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4207150936126709},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4200098514556885},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4198516607284546},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3732859790325165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7928159832954407},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7613444328308105},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6772432923316956},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.668748676776886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5855196714401245},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49797797203063965},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4760846197605133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46171805262565613},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4588601589202881},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.4500456750392914},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.42763805389404297},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4207150936126709},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4200098514556885},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4198516607284546},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3732859790325165},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped.2019.8824934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped.2019.8824934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2192203593","https://openalex.org/W2221104225","https://openalex.org/W2302255633","https://openalex.org/W2553303224","https://openalex.org/W2808938483","https://openalex.org/W2963125010","https://openalex.org/W2963240979","https://openalex.org/W2963446712","https://openalex.org/W2963821229","https://openalex.org/W2963918968","https://openalex.org/W2963993763","https://openalex.org/W2964081807","https://openalex.org/W2964350391","https://openalex.org/W3118608800","https://openalex.org/W4298342842","https://openalex.org/W6694260854","https://openalex.org/W6729956949","https://openalex.org/W6741414320","https://openalex.org/W6746582238","https://openalex.org/W6746586578","https://openalex.org/W6754259177","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2477876258","https://openalex.org/W4249307902","https://openalex.org/W2099222495","https://openalex.org/W4385571797","https://openalex.org/W2049400599","https://openalex.org/W1511852063","https://openalex.org/W4378499136","https://openalex.org/W3125825306","https://openalex.org/W2655665112","https://openalex.org/W179829755"],"abstract_inverted_index":{"Embedded":[0],"deep":[1,59],"learning":[2,60],"platforms":[3,61,164,213],"have":[4],"witnessed":[5],"two":[6],"simultaneous":[7],"improvements.":[8],"First,":[9],"the":[10,22,64,87,92,134,152,176,182,190,194,201],"accuracy":[11,115],"of":[12,24,66,79,108,116,196,205,211],"convolutional":[13],"neural":[14],"networks":[15],"(CNNs)":[16],"has":[17,36],"been":[18,37],"significantly":[19],"improved":[20,48],"through":[21],"use":[23],"automated":[25],"neural-architecture":[26],"search":[27,191],"(NAS)":[28],"algorithms":[29],"to":[30,55,85,150,184,219],"determine":[31],"CNN":[32,117],"structure.":[33],"Second,":[34],"there":[35],"increasing":[38],"interest":[39],"in":[40,63,91,189],"developing":[41],"hardware":[42,89,131,187,212,218],"accelerators":[43],"for":[44,158],"CNNs":[45],"that":[46,180],"provide":[47],"inference":[49],"performance":[50,75],"and":[51,69,76,113,125,141,144,168],"energy":[52,77,111,124,142],"consumption":[53,78,143],"compared":[54],"GPUs.":[56],"Such":[57],"embedded":[58,120,130,163],"differ":[62],"amount":[65],"compute":[67],"resources":[68,90],"memory-access":[70],"bandwidth,":[71],"which":[72],"would":[73],"affect":[74],"CNNs.":[80],"It":[81],"is":[82,200],"therefore":[83],"critical":[84],"consider":[86,186],"available":[88],"network":[93],"architecture":[94],"search.":[95],"To":[96,193],"this":[97,199],"end,":[98],"we":[99],"introduce":[100],"TEA-DNN,":[101],"a":[102,209],"NAS":[103],"algorithm":[104],"targeting":[105],"multi-objective":[106],"optimization":[107],"execution":[109,126,139],"time,":[110,140],"consumption,":[112],"classification":[114,160],"workloads":[118],"on":[119,129,161,217],"architectures.":[121],"TEA-DNN":[122,157],"leverages":[123],"time":[127],"measurements":[128,216],"when":[132],"exploring":[133],"Pareto-optimal":[135,177,206],"curves":[136],"across":[137,208],"accuracy,":[138],"does":[145],"not":[146],"require":[147],"additional":[148],"effort":[149],"model":[151],"underlying":[153],"hardware.":[154],"We":[155,174],"apply":[156],"image":[159],"actual":[162,215],"(NVIDIA":[165],"Jetson":[166],"TX2":[167],"Intel":[169],"Movidius":[170],"Neural":[171],"Compute":[172],"Stick).":[173],"highlight":[175],"operating":[178],"points":[179],"emphasize":[181],"necessity":[183],"explicitly":[185],"characteristics":[188],"process.":[192],"best":[195],"our":[197],"knowledge,":[198],"most":[202],"comprehensive":[203],"study":[204],"models":[207],"range":[210],"using":[214],"obtain":[220],"objective":[221],"values.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
