{"id":"https://openalex.org/W7140202783","doi":"https://doi.org/10.48550/arxiv.2603.20920","title":"Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing","display_name":"Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140202783","doi":"https://doi.org/10.48550/arxiv.2603.20920"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20920","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20920","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.20920","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Amin, Lisan Al","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amin, Lisan Al","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hossain, Md Ismail","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hossain, Md Ismail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Das, Rupak Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Rupak Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Islam, Mahbubul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Islam, Mahbubul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Tabbakh, Abdulaziz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tabbakh, Abdulaziz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5148000121116638,"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":0.5148000121116638,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.13459999859333038,"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"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.11209999769926071,"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/deep-learning","display_name":"Deep learning","score":0.6973000168800354},{"id":"https://openalex.org/keywords/xeon","display_name":"Xeon","score":0.664900004863739},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5514000058174133},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.48260000348091125},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.4616999924182892},{"id":"https://openalex.org/keywords/general-purpose-computing-on-graphics-processing-units","display_name":"General-purpose computing on graphics processing units","score":0.4431999921798706},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.4259999990463257},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.41999998688697815},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.38749998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8090000152587891},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6973000168800354},{"id":"https://openalex.org/C145108525","wikidata":"https://www.wikidata.org/wiki/Q656154","display_name":"Xeon","level":2,"score":0.664900004863739},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.546500027179718},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4968000054359436},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C50630238","wikidata":"https://www.wikidata.org/wiki/Q971505","display_name":"General-purpose computing on graphics processing units","level":3,"score":0.4431999921798706},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3896999955177307},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C170723468","wikidata":"https://www.wikidata.org/wiki/Q182933","display_name":"x86","level":3,"score":0.3587999939918518},{"id":"https://openalex.org/C96972482","wikidata":"https://www.wikidata.org/wiki/Q1049168","display_name":"Xeon Phi","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C83283714","wikidata":"https://www.wikidata.org/wiki/Q121117","display_name":"Supercomputer","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2780368719","wikidata":"https://www.wikidata.org/wiki/Q5157342","display_name":"Computational thinking","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20920","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20920","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.20920","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20920","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6550887227058411,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"exponential":[1],"growth":[2,21],"in":[3,22,125],"data":[4],"has":[5],"intensified":[6],"the":[7,19,101],"demand":[8],"for":[9,49,166,179],"computational":[10,35,187],"power":[11],"to":[12,34,90,121,174],"train":[13],"large-scale":[14],"deep":[15,57],"learning":[16,58],"models.":[17],"However,":[18],"rapid":[20],"model":[23,94],"size":[24],"and":[25,31,41,66,72,112,150,171],"complexity":[26],"raises":[27],"concerns":[28],"about":[29],"equal":[30],"fair":[32],"access":[33,173],"resources,":[36],"particularly":[37],"under":[38],"increasing":[39],"energy":[40],"infrastructure":[42],"constraints.":[43],"GPUs":[44,163],"have":[45],"emerged":[46],"as":[47],"essential":[48,165],"accelerating":[50],"such":[51],"workloads.":[52],"This":[53],"study":[54],"benchmarks":[55],"four":[56],"models":[59,98,106,115],"(Conv6,":[60],"VGG16,":[61],"ResNet18,":[62],"CycleGAN)":[63],"using":[64,155],"TensorFlow":[65,129],"PyTorch":[67,127],"on":[68,81,93],"Intel":[69],"Xeon":[70],"CPUs":[71],"NVIDIA":[73],"Tesla":[74],"T4":[75],"GPUs.":[76],"Our":[77,158],"experiments":[78],"demonstrate":[79],"that,":[80],"average,":[82],"GPU":[83,146,175],"training":[84],"achieves":[85],"speedups":[86],"ranging":[87],"from":[88],"11x":[89,118],"246x":[91],"depending":[92],"complexity,":[95],"with":[96,185],"lightweight":[97],"(Conv6)":[99],"showing":[100],"highest":[102],"acceleration":[103],"(246x),":[104],"mid-sized":[105],"(VGG16,":[107],"ResNet18)":[108],"achieving":[109],"51-116x":[110],"speedups,":[111],"complex":[113],"generative":[114],"(CycleGAN)":[116],"reaching":[117],"improvements":[119],"compared":[120],"CPU":[122],"training.":[123],"Additionally,":[124],"our":[126],"vs.":[128],"comparison,":[130],"we":[131],"observed":[132],"that":[133,161],"TensorFlow's":[134],"kernel-fusion":[135],"optimizations":[136],"reduce":[137],"inference":[138],"latency":[139],"by":[140],"approximately":[141],"15%.":[142],"We":[143],"also":[144],"analyze":[145],"memory":[147],"usage":[148],"trends":[149],"projecting":[151],"requirements":[152],"through":[153],"2025":[154],"polynomial":[156],"regression.":[157],"findings":[159],"highlight":[160],"while":[162],"are":[164],"sustaining":[167],"AI's":[168],"growth,":[169],"democratized":[170],"shared":[172],"resources":[176],"is":[177],"critical":[178],"enabling":[180],"research":[181],"innovation":[182],"across":[183],"institutions":[184],"limited":[186],"budgets.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}
