{"id":"https://openalex.org/W4388561325","doi":"https://doi.org/10.1145/3624062.3624258","title":"Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices","display_name":"Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices","publication_year":2023,"publication_date":"2023-11-10","ids":{"openalex":"https://openalex.org/W4388561325","doi":"https://doi.org/10.1145/3624062.3624258"},"language":"en","primary_location":{"id":"doi:10.1145/3624062.3624258","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3624062.3624258","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3624062.3624258","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3624062.3624258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100697206","display_name":"Yuke Li","orcid":"https://orcid.org/0009-0009-5806-7228"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuke Li","raw_affiliation_strings":["University of California, Merced, United States of America"],"affiliations":[{"raw_affiliation_string":"University of California, Merced, United States of America","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026150355","display_name":"Jiwon Baik","orcid":"https://orcid.org/0000-0003-2366-7601"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiwon Baik","raw_affiliation_strings":["University of California, Santa Barbara, United States of America"],"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, United States of America","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014305826","display_name":"Md Rahman","orcid":"https://orcid.org/0000-0001-8402-420X"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Marufi Rahman","raw_affiliation_strings":["University of North Texas, USA"],"affiliations":[{"raw_affiliation_string":"University of North Texas, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052217926","display_name":"Iraklis Anagnostopoulos","orcid":"https://orcid.org/0000-0003-0985-3045"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iraklis Anagnostopoulos","raw_affiliation_strings":["Southern Illinois University, USA"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078240422","display_name":"Ruopu Li","orcid":"https://orcid.org/0000-0003-3500-0273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruopu Li","raw_affiliation_strings":["Southern Illinois University, USA"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056821569","display_name":"Tong Shu","orcid":"https://orcid.org/0000-0001-8617-1772"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Shu","raw_affiliation_strings":["University of North Texas, United States of America"],"affiliations":[{"raw_affiliation_string":"University of North Texas, United States of America","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100697206"],"corresponding_institution_ids":["https://openalex.org/I156087764"],"apc_list":null,"apc_paid":null,"fwci":1.4719,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.82431885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1767","last_page":"1775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10577","display_name":"Hydrology and Sediment Transport Processes","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10577","display_name":"Hydrology and Sediment Transport Processes","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7845895886421204},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7628748416900635},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7122493982315063},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.6096848249435425},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5492708683013916},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5318811535835266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4891720116138458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4857216477394104},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4549238085746765},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4288107454776764},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.4262194335460663},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.39198994636535645},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34588637948036194},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11139556765556335}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7845895886421204},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7628748416900635},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7122493982315063},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.6096848249435425},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5492708683013916},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5318811535835266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4891720116138458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4857216477394104},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4549238085746765},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4288107454776764},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.4262194335460663},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.39198994636535645},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34588637948036194},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11139556765556335},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3624062.3624258","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3624062.3624258","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3624062.3624258","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis","raw_type":"proceedings-article"},{"id":"pmh:info:ark/67531/metadc2289473","is_oa":true,"landing_page_url":"https://digital.library.unt.edu/ark:/67531/metadc2289473/","pdf_url":"https://digital.library.unt.edu/ark:/67531/metadc2289473/m2/1/high_res_d/3624062.3624258.pdf","source":{"id":"https://openalex.org/S4306400792","display_name":"University of North Texas Digital Library (University of North Texas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I123534392","host_organization_name":"University of North Texas","host_organization_lineage":["https://openalex.org/I123534392"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, November 12-17, 2023. Denver, CO, United States","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1145/3624062.3624258","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3624062.3624258","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3624062.3624258","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3437180546","display_name":null,"funder_award_id":"2306184, 1951741","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G3923958600","display_name":null,"funder_award_id":"1951741","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7701883343","display_name":"Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure","funder_award_id":"2306184","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320310911","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388561325.pdf","grobid_xml":"https://content.openalex.org/works/W4388561325.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1662323679","https://openalex.org/W1965609748","https://openalex.org/W1965849340","https://openalex.org/W1981251392","https://openalex.org/W2049490821","https://openalex.org/W2077509829","https://openalex.org/W2112081648","https://openalex.org/W2116661285","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2913064927","https://openalex.org/W2927980542","https://openalex.org/W2965658867","https://openalex.org/W2966284335","https://openalex.org/W2989983865","https://openalex.org/W3032945613","https://openalex.org/W3046853140","https://openalex.org/W3129852841","https://openalex.org/W3165698711","https://openalex.org/W3194922745","https://openalex.org/W3209828932","https://openalex.org/W3210365813","https://openalex.org/W4285588262","https://openalex.org/W4288436645","https://openalex.org/W4307827570","https://openalex.org/W4362566357","https://openalex.org/W4381389677","https://openalex.org/W4382931623","https://openalex.org/W4388855541"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2011676020","https://openalex.org/W2329895846"],"abstract_inverted_index":{"Embedded":[0],"devices,":[1],"constrained":[2],"by":[3,53,72,127],"limited":[4],"memory":[5,42,97],"and":[6,44,96,134],"processors,":[7],"require":[8],"deep":[9],"learning":[10],"models":[11,105],"to":[12,15,37,115],"be":[13],"tailored":[14],"their":[16],"specifications.":[17],"This":[18],"research":[19],"explores":[20],"customized":[21],"model":[22],"architectures":[23],"for":[24,137],"classifying":[25],"drainage":[26],"crossing":[27],"images.":[28],"Building":[29],"on":[30],"the":[31,116,132],"foundational":[32],"ResNet-18,":[33],"this":[34],"paper":[35,125],"aims":[36],"maximize":[38],"prediction":[39],"accuracy,":[40,94,110],"reduce":[41],"size,":[43],"minimize":[45],"inference":[46,79],"latency.":[47],"Various":[48],"configurations":[49],"were":[50],"systematically":[51],"probed":[52],"leveraging":[54],"hardware-aware":[55],"neural":[56],"architecture":[57],"search,":[58],"accumulating":[59],"1,717":[60],"experimental":[61,68],"results":[62,133],"over":[63],"six":[64],"benchmarking":[65],"variants.":[66],"The":[67,124],"data":[69],"analysis,":[70],"enhanced":[71],"nn-Meter,":[73],"provided":[74],"a":[75,86,112],"comprehensive":[76],"understanding":[77],"of":[78,93],"latency":[80],"across":[81],"four":[82],"different":[83],"predictors.":[84],"Significantly,":[85],"Pareto":[87],"front":[88],"analysis":[89],"with":[90],"three":[91],"objectives":[92],"latency,":[95],"resulted":[98],"in":[99,121],"five":[100],"non-dominated":[101],"solutions.":[102],"These":[103],"standout":[104],"showcased":[106],"efficiency":[107],"while":[108],"retaining":[109],"offering":[111],"compelling":[113],"alternative":[114],"conventional":[117],"ResNet-18":[118],"when":[119],"deployed":[120],"resource-constrained":[122],"environments.":[123],"concludes":[126],"highlighting":[128],"insights":[129],"drawn":[130],"from":[131],"suggesting":[135],"avenues":[136],"future":[138],"exploration.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
