{"id":"https://openalex.org/W7130596516","doi":"https://doi.org/10.1109/ictc66702.2025.11388617","title":"Comparative Study of Convolution Methods on Embedded AI Platforms for Inference Efficiency","display_name":"Comparative Study of Convolution Methods on Embedded AI Platforms for Inference Efficiency","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7130596516","doi":"https://doi.org/10.1109/ictc66702.2025.11388617"},"language":null,"primary_location":{"id":"doi:10.1109/ictc66702.2025.11388617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11388617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5074770161","display_name":"Mooseop Kim","orcid":"https://orcid.org/0000-0003-4914-0584"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Mooseop Kim","raw_affiliation_strings":["Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109604416","display_name":"Youngmi Song","orcid":"https://orcid.org/0009-0003-9576-9356"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngmi Song","raw_affiliation_strings":["Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005862193","display_name":"Chi Yoon Jeong","orcid":"https://orcid.org/0000-0001-7089-2516"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chi Yoon Jeong","raw_affiliation_strings":["Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124357765","display_name":"SuGil Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"SuGil Choi","raw_affiliation_strings":["Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute,Human Sensory Augmentation Research Section,Daejeon,Republic of Korea","institution_ids":["https://openalex.org/I142401562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142401562"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"73","last_page":"76"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.35830000042915344,"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":0.35830000042915344,"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/T14347","display_name":"Big Data and Digital Economy","score":0.2583000063896179,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.0674000009894371,"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/convolution","display_name":"Convolution (computer science)","score":0.7540000081062317},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6740000247955322},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.621999979019165},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.6039000153541565},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5889000296592712},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5228999853134155},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5180000066757202}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7540000081062317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7397000193595886},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6740000247955322},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6039000153541565},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5889000296592712},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5228999853134155},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5180000066757202},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4896000027656555},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.38679999113082886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3456000089645386},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C118993495","wikidata":"https://www.wikidata.org/wiki/Q5042828","display_name":"Electrical efficiency","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3287000060081482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.31220000982284546},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27790001034736633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2630000114440918},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc66702.2025.11388617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11388617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6497387290000916}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2172654076","https://openalex.org/W2604319603","https://openalex.org/W2626991402","https://openalex.org/W2963114857","https://openalex.org/W2970959587","https://openalex.org/W3080404596","https://openalex.org/W3216299549","https://openalex.org/W4313555487","https://openalex.org/W4366723544","https://openalex.org/W4366975778","https://openalex.org/W4382203333"],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"deployment":[1],"of":[2,12,26],"deep":[3],"learning":[4],"models":[5],"on":[6],"embedded":[7,39,159],"systems":[8],"requires":[9],"careful":[10],"consideration":[11],"inference":[13],"latency,":[14,71],"resource":[15],"utilization,":[16],"and":[17,33,44,56,63,78,81,113,144],"energy":[18,82,123],"efficiency.":[19],"This":[20],"paper":[21],"presents":[22],"an":[23],"empirical":[24],"comparison":[25],"three":[27],"convolution":[28,96,105,115,155],"approaches\u2014Direct":[29],"convolution,":[30,32],"GEMM-based":[31,104],"the":[34,98],"Winograd":[35,114],"algorithm\u2014on":[36],"two":[37],"representative":[38],"AI":[40,160],"platforms:":[41],"Jetson":[42],"Nano":[43],"ODROID-XU4.":[45],"Each":[46],"method":[47],"is":[48],"evaluated":[49],"under":[50,125],"various":[51],"execution":[52,145],"modes":[53],"(CPU-only,":[54],"GPU-only,":[55],"CPU+GPU":[57],"hybrid)":[58],"across":[59],"multiple":[60],"input":[61],"sizes":[62],"batch":[64],"configurations.":[65],"The":[66,91],"key":[67],"performance":[68],"metrics":[69],"including":[70],"throughput":[72,108],"(FPS),":[73],"hardware":[74],"utilization":[75],"(CPU/GPU":[76],"usage":[77],"memory":[79,111],"consumption),":[80],"efficiency":[83,124],"(FPS/Watt)":[84],"are":[85],"measured":[86],"to":[87],"assess":[88],"practical":[89,150],"viability.":[90],"results":[92],"reveal":[93],"that":[94,137],"Direct":[95],"achieves":[97],"lowest":[99],"latency":[100,118],"but":[101,120],"scales":[102],"poorly,":[103],"offers":[106],"strong":[107],"with":[109],"higher":[110],"demands,":[112],"shows":[116],"limited":[117],"benefits":[119],"excels":[121],"in":[122,157],"certain":[126],"conditions.":[127],"Power":[128,134],"consumption":[129],"analysis":[130],"using":[131],"a":[132],"Monsoon":[133],"Monitor":[135],"confirms":[136],"optimal":[138],"energy-performance":[139],"trade-offs":[140],"vary":[141],"by":[142],"platform":[143],"strategy.":[146],"These":[147],"findings":[148],"provide":[149],"guidance":[151],"for":[152],"selecting":[153],"appropriate":[154],"strategies":[156],"resource-constrained":[158],"deployments.":[161]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-20T00:00:00"}