{"id":"https://openalex.org/W4367318869","doi":"https://doi.org/10.1145/3543873.3587370","title":"Hierarchical Deep Neural Network Inference for Device-Edge-Cloud Systems","display_name":"Hierarchical Deep Neural Network Inference for Device-Edge-Cloud Systems","publication_year":2023,"publication_date":"2023-04-28","ids":{"openalex":"https://openalex.org/W4367318869","doi":"https://doi.org/10.1145/3543873.3587370"},"language":"en","primary_location":{"id":"doi:10.1145/3543873.3587370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543873.3587370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2023","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/A5007420449","display_name":"Fatih \u0130lhan","orcid":"https://orcid.org/0000-0002-0173-7544"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fatih Ilhan","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053032757","display_name":"Selim Furkan Tekin","orcid":"https://orcid.org/0000-0002-8662-3609"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Selim Furkan Tekin","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071522360","display_name":"Sihao Hu","orcid":"https://orcid.org/0000-0003-3297-6991"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sihao Hu","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020135180","display_name":"Tiansheng Huang","orcid":"https://orcid.org/0000-0002-4557-1865"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiansheng Huang","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028240524","display_name":"Ka-Ho Chow","orcid":"https://orcid.org/0000-0001-5917-2577"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ka-Ho Chow","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343991","display_name":"Ling Liu","orcid":"https://orcid.org/0000-0002-4138-3082"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Liu","raw_affiliation_strings":["Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5007420449"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.6027,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66521028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"302","last_page":"305"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9998000264167786,"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":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9951000213623047,"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/T13553","display_name":"Age of Information Optimization","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8416518568992615},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8399131894111633},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6769999265670776},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.665062665939331},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.661358118057251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6491347551345825},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6013297438621521},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5606675148010254},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5567286014556885},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5392929315567017},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.5373415350914001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4331844449043274},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22898134589195251},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.09232300519943237}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8416518568992615},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8399131894111633},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6769999265670776},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.665062665939331},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.661358118057251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6491347551345825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6013297438621521},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5606675148010254},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5567286014556885},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5392929315567017},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.5373415350914001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4331844449043274},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22898134589195251},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09232300519943237},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3543873.3587370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543873.3587370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/343423","is_oa":false,"landing_page_url":"https://hub.hku.hk/handle/10722/343423","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"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":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1516184288","https://openalex.org/W1821462560","https://openalex.org/W2108598243","https://openalex.org/W2251939518","https://openalex.org/W2781528640","https://openalex.org/W3168652588","https://openalex.org/W4248587610","https://openalex.org/W4281760668"],"related_works":["https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W4313463218","https://openalex.org/W3106131444","https://openalex.org/W3216099748","https://openalex.org/W4205963435","https://openalex.org/W4312996489","https://openalex.org/W3214037210"],"abstract_inverted_index":{"Edge":[0],"computing":[1,4],"and":[2,22,111,121],"cloud":[3],"have":[5],"been":[6],"utilized":[7],"in":[8,12],"many":[9],"AI":[10],"applications":[11],"various":[13],"fields,":[14],"such":[15],"as":[16],"computer":[17],"vision,":[18],"NLP,":[19],"autonomous":[20],"driving,":[21],"smart":[23],"cities.":[24],"To":[25],"benefit":[26],"from":[27],"the":[28,51,58,85,98],"advantages":[29],"of":[30,53,60],"both":[31,109],"paradigms,":[32],"we":[33,102],"introduce":[34,103],"HiDEC,":[35],"a":[36,54,67,104],"hierarchical":[37],"deep":[38],"neural":[39],"network":[40],"(DNN)":[41],"inference":[42,69,89],"framework":[43],"with":[44,93],"three":[45],"novel":[46],"features.":[47],"First,":[48],"HiDEC":[49,65,127],"enables":[50],"training":[52],"resource-adaptive":[55],"DNN":[56],"through":[57],"injection":[59],"multiple":[61],"early":[62],"exits.":[63],"Second,":[64],"provides":[66],"latency-aware":[68],"scheduler,":[70],"which":[71,73],"determines":[72],"input":[74],"samples":[75,113],"should":[76],"exit":[77,86,115],"locally":[78],"on":[79,84,90,119],"an":[80],"edge":[81,91],"device":[82],"based":[83],"scores,":[87],"enabling":[88],"devices":[92],"insufficient":[94],"resources":[95],"to":[96,114],"run":[97],"full":[99],"model.":[100],"Third,":[101],"dual":[105],"thresholding":[106],"approach":[107],"allowing":[108],"easy":[110],"difficult":[112],"early.":[116],"Our":[117],"experiments":[118],"image":[120],"text":[122],"classification":[123],"benchmarks":[124],"show":[125],"that":[126],"significantly":[128],"outperforms":[129],"existing":[130],"solutions.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}