{"id":"https://openalex.org/W7154490149","doi":"https://doi.org/10.48550/arxiv.2604.12891","title":"TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning","display_name":"TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154490149","doi":"https://doi.org/10.48550/arxiv.2604.12891"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12891","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.2604.12891","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120951361","display_name":"Chaoyao Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Chaoyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133655568","display_name":"Linfeng Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Linfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021445324","display_name":"Yixian Shen","orcid":"https://orcid.org/0000-0001-8447-872X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yixian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133631571","display_name":"Tao Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133697212","display_name":"Guoqing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guoqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067055700","display_name":"Anuj Pathania","orcid":"https://orcid.org/0000-0002-5813-7021"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pathania, Anuj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084046597","display_name":"Andy D. Pimentel","orcid":"https://orcid.org/0000-0002-2043-4469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pimentel, Andy D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133697584","display_name":"Meng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Meng","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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.3131999969482422,"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"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.3131999969482422,"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/T12303","display_name":"Tensor decomposition and applications","score":0.24130000174045563,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.06970000267028809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/compiler","display_name":"Compiler","score":0.5149000287055969},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5077000260353088},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.447299987077713},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4350000023841858},{"id":"https://openalex.org/keywords/multi-core-processor","display_name":"Multi-core processor","score":0.4108000099658966},{"id":"https://openalex.org/keywords/optimizing-compiler","display_name":"Optimizing compiler","score":0.35589998960494995},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.35109999775886536},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.34790000319480896},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3395000100135803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033000230789185},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.5149000287055969},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C78766204","wikidata":"https://www.wikidata.org/wiki/Q555032","display_name":"Multi-core processor","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37720000743865967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35749998688697815},{"id":"https://openalex.org/C190902152","wikidata":"https://www.wikidata.org/wiki/Q1325106","display_name":"Optimizing compiler","level":3,"score":0.35589998960494995},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C139571649","wikidata":"https://www.wikidata.org/wiki/Q1156793","display_name":"Program optimization","level":3,"score":0.32919999957084656},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3086000084877014},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C2989134064","wikidata":"https://www.wikidata.org/wiki/Q288510","display_name":"Execution time","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C49154492","wikidata":"https://www.wikidata.org/wiki/Q5300","display_name":"Central processing unit","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12891","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.2604.12891","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12891","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,76],"(DL)":[2],"compilers":[3],"rely":[4],"on":[5,21,65,186,194],"cost":[6,106,124],"models":[7,185],"and":[8,29,44,90,122,128,132,141,154,173,189,197,202,204],"auto-tuning":[9],"to":[10,57,211],"optimize":[11],"tensor":[12,50,83],"programs":[13,84],"for":[14,48],"target":[15],"hardware.":[16],"However,":[17],"existing":[18],"approaches":[19],"depend":[20],"large":[22],"offline":[23],"datasets,":[24],"incurring":[25],"high":[26],"collection":[27,95],"costs":[28,96],"offering":[30],"suboptimal":[31],"transferability":[32],"across":[33,53,145],"platforms.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"introduce":[39],"TCL,":[40],"a":[41,73,103,116,134,180],"novel":[42],"efficient":[43],"transferable":[45],"compiler":[46],"framework":[47,138],"fast":[49],"program":[51],"optimization":[52],"diverse":[54],"hardware":[55,147],"platforms":[56,148],"address":[58],"these":[59],"challenges.":[60],"Specifically,":[61],"TCL":[62,176,192],"is":[63],"built":[64],"three":[66],"core":[67],"enablers:":[68],"(1)":[69],"the":[70,151,167,174],"RDU":[71],"Sampler,":[72],"data-efficient":[74],"active":[75],"strategy":[77],"that":[78,108,139],"selects":[79],"only":[80],"10%":[81],"of":[82,169,182],"by":[85,160],"jointly":[86],"optimizing":[87,179],"Representativeness,":[88],"Diversity,":[89],"Uncertainty,":[91],"substantially":[92],"reducing":[93],"data":[94,155],"while":[97,114,149],"maintaining":[98],"near-original":[99],"model":[100,107],"accuracy;":[101],"(2)":[102],"new":[104],"Mamba-based":[105],"efficiently":[109],"captures":[110],"long-range":[111],"schedule":[112],"dependencies":[113],"achieving":[115],"favorable":[117],"trade-off":[118],"between":[119],"prediction":[120],"accuracy":[121],"computational":[123],"through":[125],"reduced":[126],"parameterization":[127],"lightweight":[129],"sequence":[130],"modeling;":[131],"(3)":[133],"continuous":[135],"knowledge":[136,144],"distillation":[137],"effectively":[140],"progressively":[142],"transfers":[143],"multiple":[146],"avoiding":[150],"parameter":[152],"explosion":[153],"dependency":[156],"issues":[157],"typically":[158],"caused":[159],"traditional":[161],"multi-task":[162],"learning.":[163],"Extensive":[164],"experiments":[165],"validate":[166],"effectiveness":[168],"each":[170],"individual":[171],"enabler":[172],"holistic":[175],"framework.":[177],"When":[178],"range":[181],"mainstream":[183],"DL":[184],"both":[187],"CPU":[188],"GPU":[190],"platforms,":[191],"achieves,":[193],"average,":[195],"16.8x":[196],"12.48x":[198],"faster":[199],"tuning":[200],"time,":[201],"1.20x":[203],"1.13x":[205],"lower":[206],"inference":[207],"latency,":[208],"respectively,":[209],"compared":[210],"Tenset-MLP.":[212]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}