{"id":"https://openalex.org/W3183705277","doi":"https://doi.org/10.1109/access.2021.3123938","title":"An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling","display_name":"An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3183705277","doi":"https://doi.org/10.1109/access.2021.3123938","mag":"3183705277"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3123938","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3123938","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09592768.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09592768.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072053383","display_name":"Ian Colbert","orcid":"https://orcid.org/0000-0002-1669-5519"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ian Colbert","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego. (e-mail: icolbert@eng.ucsd.edu)","University of California\u2014San Diego"],"raw_orcid":"https://orcid.org/0000-0002-1669-5519","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego. (e-mail: icolbert@eng.ucsd.edu)","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108695226","display_name":"Ken Kreutz-Delgado","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kenneth Kreutz-Delgado","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego","University of California\u2014San Diego"],"raw_orcid":"https://orcid.org/0000-0001-8135-6823","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070446698","display_name":"Srinjoy Das","orcid":"https://orcid.org/0000-0003-3821-8112"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]},{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srinjoy Das","raw_affiliation_strings":["School of Mathematics and Data Sciences, West Virginia University","University of California\u2014San Diego"],"raw_orcid":"https://orcid.org/0000-0003-3821-8112","affiliations":[{"raw_affiliation_string":"School of Mathematics and Data Sciences, West Virginia University","institution_ids":["https://openalex.org/I12097938"]},{"raw_affiliation_string":"University of California\u2014San Diego","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2075,"currency":"USD","value_usd":2075},"apc_paid":{"value":2075,"currency":"USD","value_usd":2075},"fwci":0.0915,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38238873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"9","issue":null,"first_page":"147967","last_page":"147984"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.7436084151268005},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.7211393117904663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6883456707000732},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6272343397140503},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5756730437278748},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5140513181686401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4033997654914856},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37365150451660156},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32880234718322754},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19280195236206055},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18103447556495667},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10114207863807678}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7436084151268005},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.7211393117904663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883456707000732},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6272343397140503},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5756730437278748},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5140513181686401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4033997654914856},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37365150451660156},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32880234718322754},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19280195236206055},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18103447556495667},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10114207863807678},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/access.2021.3123938","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3123938","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09592768.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2107.07647","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2107.07647","pdf_url":"https://arxiv.org/pdf/2107.07647","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:6918181698ef4588b0398f020c59e7d9","is_oa":true,"landing_page_url":"https://doaj.org/article/6918181698ef4588b0398f020c59e7d9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 147967-147984 (2021)","raw_type":"article"},{"id":"doi:10.48550/arxiv.2107.07647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2107.07647","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:3183705277","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.1109/access.2021.3123938","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3123938","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09592768.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3183705277.pdf","grobid_xml":"https://content.openalex.org/works/W3183705277.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1903029394","https://openalex.org/W1973788353","https://openalex.org/W1999085092","https://openalex.org/W2002555321","https://openalex.org/W2023415862","https://openalex.org/W2037460365","https://openalex.org/W2094756095","https://openalex.org/W2099471712","https://openalex.org/W2121927366","https://openalex.org/W2131218797","https://openalex.org/W2172654076","https://openalex.org/W2293078015","https://openalex.org/W2304648132","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2535388113","https://openalex.org/W2583383421","https://openalex.org/W2584311934","https://openalex.org/W2604319603","https://openalex.org/W2613577722","https://openalex.org/W2791121285","https://openalex.org/W2798838583","https://openalex.org/W2912268344","https://openalex.org/W2916798096","https://openalex.org/W2945580137","https://openalex.org/W2951924105","https://openalex.org/W2954930822","https://openalex.org/W2983872911","https://openalex.org/W3013529009","https://openalex.org/W3035072672","https://openalex.org/W3111973136","https://openalex.org/W4250908627","https://openalex.org/W6649797076","https://openalex.org/W6697974390","https://openalex.org/W6703995556","https://openalex.org/W6727553916","https://openalex.org/W6737901870","https://openalex.org/W6740873641","https://openalex.org/W6745560452","https://openalex.org/W6750122613","https://openalex.org/W6758681311","https://openalex.org/W6766978945","https://openalex.org/W6770190653","https://openalex.org/W6780536331","https://openalex.org/W6783460666","https://openalex.org/W6787140958","https://openalex.org/W6789834670"],"related_works":["https://openalex.org/W3208046672","https://openalex.org/W2776500869","https://openalex.org/W3114239827","https://openalex.org/W2966818196","https://openalex.org/W2962595906","https://openalex.org/W3163738807","https://openalex.org/W3046798614","https://openalex.org/W3176937888","https://openalex.org/W3164412977","https://openalex.org/W3034724715","https://openalex.org/W2239661784","https://openalex.org/W3160164583","https://openalex.org/W2017522196","https://openalex.org/W2995332782","https://openalex.org/W2130975789","https://openalex.org/W2964210333","https://openalex.org/W3118210184","https://openalex.org/W3187276661","https://openalex.org/W2607372796","https://openalex.org/W3158418709"],"abstract_inverted_index":{"State-of-the-art":[0],"deep":[1,207],"learning":[2],"solutions":[3],"for":[4,89,215],"image":[5,125,151],"upsampling":[6,152],"are":[7],"currently":[8],"trained":[9],"using":[10,182,213],"either":[11],"resize":[12,69,240],"or":[13,70,225,232,239],"sub-pixel":[14,71,238],"convolution":[15,33,72,81,241],"to":[16,73,83,108,114,164,204,237],"learn":[17],"kernels":[18,34,82,85],"that":[19,40,64,212],"generate":[20],"high":[21],"fidelity":[22,126],"images":[23,176],"with":[24,30],"minimal":[25],"artifacts.":[26],"However,":[27],"performing":[28],"inference":[29,90,139,146,159,216],"these":[31,149],"learned":[32,80,127],"requires":[35],"memory-intensive":[36],"feature":[37],"map":[38],"transformations":[39],"dominate":[41],"time":[42,200],"and":[43,154,201,227],"energy":[44,202,228],"costs":[45,203],"in":[46,75,118,130],"real-time":[47],"applications.":[48],"To":[49,167],"alleviate":[50],"this":[51,206],"pressure":[52],"on":[53],"memory":[54],"bandwidth,":[55],"we":[56,162,174,210],"propose":[57],"a":[58,92,101,111,156,183],"novel":[59,157],"energy-efficient":[60],"edge":[61,219],"computing":[62],"paradigm":[63],"confines":[65],"the":[66,76,124,131,141,145,169,179,191,218],"use":[67,115],"of":[68,148,171,198],"training":[74,107,129],"cloud":[77,132],"by":[78,122,190,223,230],"transforming":[79],"deconvolution":[84,158],"before":[86],"deploying":[87],"them":[88],"as":[91,100,165],"functionally":[93],"equivalent":[94],"deconvolution.":[95],"These":[96],"kernel":[97],"transformations,":[98],"intended":[99],"one-time":[102],"cost":[103],"when":[104,128,234],"shifting":[105],"from":[106,178],"inference,":[109],"enable":[110],"systems":[112],"designer":[113],"each":[116],"algorithm":[117],"their":[119],"optimal":[120],"context":[121],"preserving":[123],"while":[133],"minimizing":[134],"data":[135],"transfer":[136],"penalties":[137],"during":[138],"at":[140,217],"edge.":[142],"We":[143],"compare":[144],"properties":[147],"convolution-based":[150],"algorithms":[153],"introduce":[155],"algorithm,":[160],"which":[161],"refer":[163],"REVD2.":[166],"demonstrate":[168],"benefits":[170],"our":[172],"approach,":[173],"upsample":[175],"selected":[177],"BSD300":[180],"dataset":[181],"pre-trained":[184],"single-image":[185],"super":[186],"resolution":[187],"network":[188],"provided":[189],"PyTorch":[192],"model":[193],"zoo.":[194],"Using":[195],"quantitative":[196],"models":[197],"incurred":[199],"analyze":[205],"neural":[208],"network,":[209],"estimate":[211],"REVD2":[214],"improves":[220],"system":[221],"latency":[222],"2.1x":[224,231],"2.8x":[226],"efficiency":[229],"2.7x":[233],"respectively":[235],"compared":[236],"counterparts.":[242]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2022-07-25T00:00:00"}