{"id":"https://openalex.org/W4212896416","doi":"https://doi.org/10.1145/3503823.3503843","title":"Comparative Evaluation of Machine Learning Inference Machines on Edge-class Devices","display_name":"Comparative Evaluation of Machine Learning Inference Machines on Edge-class Devices","publication_year":2021,"publication_date":"2021-11-26","ids":{"openalex":"https://openalex.org/W4212896416","doi":"https://doi.org/10.1145/3503823.3503843"},"language":"en","primary_location":{"id":"doi:10.1145/3503823.3503843","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503823.3503843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"25th Pan-Hellenic Conference on Informatics","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/A5090862898","display_name":"Petros Amanatidis","orcid":"https://orcid.org/0000-0002-8481-6087"},"institutions":[{"id":"https://openalex.org/I183898223","display_name":"International Hellenic University","ror":"https://ror.org/00708jp83","country_code":"GR","type":"education","lineage":["https://openalex.org/I183898223"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Petros Amanatidis","raw_affiliation_strings":["International Hellenic University, Greece"],"affiliations":[{"raw_affiliation_string":"International Hellenic University, Greece","institution_ids":["https://openalex.org/I183898223"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044138533","display_name":"George Iosifidis","orcid":"https://orcid.org/0000-0003-1001-2323"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"George Iosifidis","raw_affiliation_strings":["Delft University of Technology, Netherlands"],"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052277899","display_name":"Dimitris Karampatzakis","orcid":"https://orcid.org/0000-0003-0203-0476"},"institutions":[{"id":"https://openalex.org/I183898223","display_name":"International Hellenic University","ror":"https://ror.org/00708jp83","country_code":"GR","type":"education","lineage":["https://openalex.org/I183898223"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitris Karampatzakis","raw_affiliation_strings":["International Hellenic University, Greece"],"affiliations":[{"raw_affiliation_string":"International Hellenic University, Greece","institution_ids":["https://openalex.org/I183898223"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090862898"],"corresponding_institution_ids":["https://openalex.org/I183898223"],"apc_list":null,"apc_paid":null,"fwci":0.7955,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75631378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"102","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9733999967575073,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.6949916481971741},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6359228491783142},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6220847368240356},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6067385673522949},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5432513952255249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5352542996406555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6949916481971741},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6359228491783142},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6220847368240356},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6067385673522949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5432513952255249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352542996406555}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3503823.3503843","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503823.3503843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"25th Pan-Hellenic Conference on Informatics","raw_type":"proceedings-article"},{"id":"pmh:oai:tudelft.nl:uuid:a94878a8-b633-48d7-8fc6-a438ea0365ed","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:a94878a8-b633-48d7-8fc6-a438ea0365ed","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2416799949","https://openalex.org/W2786070938","https://openalex.org/W2960833983","https://openalex.org/W2963705844","https://openalex.org/W3034646338"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Computer":[0],"science":[1],"and":[2,16,50,61,99,104,110,137,156,177],"engineering":[3],"have":[4],"evolved":[5],"rapidly":[6],"over":[7],"the":[8,24,29,38,64,142,148,159,162,169],"last":[9],"decade":[10],"offering":[11],"innovative":[12],"Machine":[13,78],"Learning":[14,79],"frameworks":[15,60],"high-performance":[17],"hardware":[18,91],"devices.":[19],"Executing":[20],"data":[21],"analytics":[22],"at":[23],"edge":[25],"promises":[26],"to":[27,37,47,51],"transform":[28],"mobile":[30],"computing":[31],"paradigm":[32],"by":[33,154,175,180],"bringing":[34],"intelligence":[35],"next":[36],"end":[39],"user.":[40],"However,":[41],"it":[42],"remains":[43],"an":[44,114,144],"open":[45],"question":[46],"explore":[48],"if,":[49],"what":[52],"extent,":[53],"today\u2019s":[54],"Edge-class":[55,83],"devices":[56],"can":[57,151],"support":[58],"ML":[59,129],"which":[62],"is":[63],"best":[65],"configuration":[66,146],"for":[67],"efficient":[68],"task":[69],"execution.":[70],"This":[71],"paper":[72],"provides":[73],"a":[74],"comparative":[75],"evaluation":[76],"of":[77,89,117,133,147,161],"inference":[80,106,170],"machines":[81,107],"on":[82,141],"compute":[84,92,164],"engines.":[85],"The":[86],"testbed":[87],"consists":[88],"two":[90,105],"engines":[93],"(i.e.,":[94,108],"CPU-based":[95],"Raspberry":[96],"Pi":[97],"4":[98],"Google":[100],"Edge":[101,163],"TPU":[102],"accelerator)":[103],"TensorFlow-Lite":[109,128],"Arm":[111],"NN).":[112],"Through":[113],"extensive":[115],"set":[116],"experiments":[118],"in":[119,131,157,166],"our":[120],"bespoke":[121],"testbed,":[122],"we":[123],"compared":[124],"three":[125],"setups":[126],"using":[127],"framework,":[130],"terms":[132],"accuracy,":[134],"execution":[135,173],"time,":[136],"energy":[138],"efficiency.":[139],"Based":[140],"results,":[143],"optimized":[145],"workload":[149],"parameters":[150],"increase":[152],"accuracy":[153],"10%,":[155],"addition,":[158],"class":[160],"engine":[165],"combination":[167],"with":[168],"machine":[171],"affects":[172],"time":[174],"86%":[176],"power":[178],"consumption":[179],"almost":[181],"145%.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}