{"id":"https://openalex.org/W2541249978","doi":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","title":"Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs","display_name":"Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2541249978","doi":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","mag":"2541249978"},"language":"en","primary_location":{"id":"doi:10.1109/bdcloud-socialcom-sustaincom.2016.76","doi":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100384534","display_name":"Da Li","orcid":"https://orcid.org/0000-0002-1127-0642","observed_orcids":["https://orcid.org/0000-0002-1127-0642"]},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Li","raw_affiliation_strings":["Dept. of Electrical and Computer Engineering, University of Missouri"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electrical and Computer Engineering, University of Missouri","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101419808","display_name":"Xinbo Chen","orcid":"https://orcid.org/0000-0003-2155-6916","observed_orcids":["https://orcid.org/0000-0003-2155-6916"]},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinbo Chen","raw_affiliation_strings":["Dept. of Computer Science, Texas State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Texas State University","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041520129","display_name":"Michela Becchi","orcid":"https://orcid.org/0000-0001-8353-2915","observed_orcids":["https://orcid.org/0000-0001-8353-2915"]},"institutions":[{"id":"https://openalex.org/I4777552","display_name":"University of Missouri System","ror":"https://ror.org/032va1j59","country_code":"US","type":"education","lineage":["https://openalex.org/I4777552"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michela Becchi","raw_affiliation_strings":["University of Missouri System, Columbia, MO, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri System, Columbia, MO, US","institution_ids":["https://openalex.org/I4777552"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008451482","display_name":"Ziliang Zong","orcid":"https://orcid.org/0000-0003-2693-7419","observed_orcids":["https://orcid.org/0000-0003-2693-7419"]},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziliang Zong","raw_affiliation_strings":["Dept. of Computer Science, Texas State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Texas State University","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0489,"has_fulltext":false,"cited_by_count":249,"citation_normalized_percentile":{"value":0.92347121,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"477","last_page":"484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.8833000063896179,"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"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.8833000063896179,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.049300000071525574,"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/T14098","display_name":"Energy Efficiency in Computing","score":0.046799998730421066,"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/energy-efficiency","display_name":"energy efficiency","score":0.9649999737739563},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.9679999947547913},{"id":"https://openalex.org/keywords/power-consumption","display_name":"power consumption","score":0.8529999852180481},{"id":"https://openalex.org/keywords/gpu-training","display_name":"GPU training","score":0.9990000128746033},{"id":"https://openalex.org/keywords/workload-analysis","display_name":"workload characterization","score":0.9750000238418579},{"id":"https://openalex.org/keywords/energy-efficient-deep-learning","display_name":"energy-efficient deep learning","score":0.7080000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8393951654434204},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8292539119720459},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7773720026016235},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6565799713134766},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6199295520782471},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5855432748794556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.554850697517395},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4888143241405487},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4698905944824219},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.43684232234954834},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4350699186325073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4284774363040924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39323461055755615},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09631964564323425},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bdcloud-socialcom-sustaincom.2016.76","doi":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.76","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.878000020980835,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W579519726","https://openalex.org/W753012316","https://openalex.org/W1487583988","https://openalex.org/W1598866093","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1757796397","https://openalex.org/W1828348983","https://openalex.org/W1980489165","https://openalex.org/W1993845689","https://openalex.org/W1995562189","https://openalex.org/W2015861736","https://openalex.org/W2040062623","https://openalex.org/W2040870580","https://openalex.org/W2069143585","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2136391815","https://openalex.org/W2141504882","https://openalex.org/W2145287260","https://openalex.org/W2155893237","https://openalex.org/W2162390675","https://openalex.org/W2163605009","https://openalex.org/W2183631084","https://openalex.org/W2186615578","https://openalex.org/W2286256158","https://openalex.org/W2289252105","https://openalex.org/W2556021649","https://openalex.org/W2963542991","https://openalex.org/W3023154643","https://openalex.org/W4236949603","https://openalex.org/W6622239757"],"related_works":["https://openalex.org/W3212586923","https://openalex.org/W2766975872","https://openalex.org/W2887275656","https://openalex.org/W2888971660","https://openalex.org/W3007579076","https://openalex.org/W3093818819","https://openalex.org/W7171641226","https://openalex.org/W7124231374","https://openalex.org/W4415328769","https://openalex.org/W4415195800"],"abstract_inverted_index":{"In":[0,116],"recent":[1],"years":[2],"convolutional":[3],"neural":[4],"networks":[5],"(CNNs)":[6],"have":[7,48],"been":[8,73],"successfully":[9],"applied":[10],"to":[11,25,102,149],"various":[12],"applications":[13],"that":[14,109],"are":[15,64,110],"appropriate":[16],"for":[17,91],"deep":[18,40,104,156],"learning,":[19],"from":[20],"image":[21],"and":[22,37,45,53,62,107,113,128,135,140,142],"video":[23],"processing":[24],"speech":[26],"recognition.":[27],"The":[28],"advancements":[29],"in":[30,76],"both":[31,111],"hardware":[32],"(e.g.":[33,39],"more":[34],"powerful":[35],"GPUs)":[36],"software":[38],"learning":[41,105,157],"models,":[42],"open-source":[43],"frameworks":[44,106,137],"supporting":[46],"libraries)":[47],"significantly":[49],"improved":[50],"the":[51,59,66,81,88,125,151],"accuracy":[52,63],"training":[54,92,136],"time":[55],"of":[56,68,83,131,153],"CNNs.":[57],"However,":[58],"high":[60],"speed":[61],"at":[65],"cost":[67],"energy":[69,89,114,129,154],"consumption,":[70],"which":[71],"has":[72],"largely":[74],"ignored":[75],"previous":[77],"CNN":[78],"design.":[79],"With":[80],"size":[82],"data":[84,94],"sets":[85,95],"grows":[86],"exponentially,":[87],"demand":[90],"such":[93],"increases":[96],"rapidly.":[97],"It":[98],"is":[99],"highly":[100],"desirable":[101],"design":[103,152],"algorithms":[108],"accurate":[112],"efficient.":[115],"this":[117],"paper,":[118],"we":[119,143],"conduct":[120],"a":[121,145],"comprehensive":[122],"study":[123],"on":[124,138],"power":[126],"behavior":[127],"efficiency":[130],"numerous":[132],"well-known":[133],"CNNs":[134],"CPUs":[139],"GPUs,":[141],"provide":[144],"detailed":[146],"workload":[147],"characterization":[148],"facilitate":[150],"efficient":[155],"solutions.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":72},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-11-04T00:00:00"}
