{"id":"https://openalex.org/W7154079399","doi":"https://doi.org/10.48550/arxiv.2604.09073","title":"DRIFT: Harnessing Inherent Fault Tolerance for Efficient and Reliable Diffusion Model Inference","display_name":"DRIFT: Harnessing Inherent Fault Tolerance for Efficient and Reliable Diffusion Model Inference","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154079399","doi":"https://doi.org/10.48550/arxiv.2604.09073"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09073","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.2604.09073","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133538894","display_name":"Jinqi Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Jinqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133552270","display_name":"Tong Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133549813","display_name":"Runsheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Runsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133488015","display_name":"Meng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, 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":0.1527000069618225,"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":0.1527000069618225,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09700000286102295,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08630000054836273,"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/fault-tolerance","display_name":"Fault tolerance","score":0.6410999894142151},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5705999732017517},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.565500020980835},{"id":"https://openalex.org/keywords/rollback","display_name":"Rollback","score":0.560699999332428},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5304999947547913},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5253000259399414},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.49140000343322754},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4309999942779541},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.3986999988555908}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7188000082969666},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.6410999894142151},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5705999732017517},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C174220543","wikidata":"https://www.wikidata.org/wiki/Q395307","display_name":"Rollback","level":3,"score":0.560699999332428},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5304999947547913},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5253000259399414},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4169999957084656},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3813999891281128},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C77019957","wikidata":"https://www.wikidata.org/wiki/Q2689057","display_name":"Dependability","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29030001163482666},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C167391956","wikidata":"https://www.wikidata.org/wiki/Q1401211","display_name":"Fault model","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C2780080018","wikidata":"https://www.wikidata.org/wiki/Q2439233","display_name":"Tolerance analysis","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C7366592","wikidata":"https://www.wikidata.org/wiki/Q1255620","display_name":"Dram","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09073","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.2604.09073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09073","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":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9109342694282532}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"model":[1,86],"deployment":[2],"has":[3],"been":[4],"suffering":[5],"from":[6],"high":[7],"energy":[8,164],"consumption":[9],"and":[10,23,83,116,118,142,154],"inference":[11],"latency":[12],"despite":[13],"its":[14],"superior":[15],"performance":[16],"in":[17,65],"visual":[18],"generation":[19,176],"tasks.":[20],"Dynamic":[21],"voltage":[22,167],"frequency":[24],"scaling":[25],"(DVFS)":[26],"offers":[27],"a":[28,71,91,105,119],"promising":[29],"solution":[30],"to":[31,44,134,146],"exploit":[32],"the":[33,36,56,61,78],"potential":[34],"of":[35,60],"underlying":[37],"accelerators.":[38],"However,":[39],"existing":[40],"approaches":[41],"often":[42],"lead":[43],"either":[45],"limited":[46],"efficiency":[47],"gains":[48],"or":[49,169],"degraded":[50],"output":[51],"quality":[52],"because":[53],"they":[54],"overlook":[55],"inherent":[57],"fault":[58,79,122],"tolerance":[59,80,123],"diffusion":[62,85,97],"model.":[63],"Therefore,":[64],"this":[66],"paper,":[67],"we":[68,103],"propose":[69],"DRIFT,":[70],"novel":[72],"algorithmarchitecture":[73],"co-optimization":[74],"framework":[75],"that":[76,110,126,157],"harnesses":[77],"for":[81],"efficient":[82],"reliable":[84],"inference.":[87],"We":[88,137],"first":[89],"perform":[90],"comprehensive":[92],"resilience":[93],"analysis":[94],"on":[95,100,161],"representative":[96],"models.":[98],"Building":[99],"these":[101],"observations,":[102],"introduce":[104],"fine-grained,":[106],"resilience-aware":[107],"DVFS":[108],"strategy":[109],"selectively":[111],"protects":[112],"error-sensitive":[113],"network":[114],"blocks":[115],"timesteps,":[117],"rollback":[120],"algorithm-based":[121],"(ABFT)":[124],"mechanism":[125],"adaptively":[127],"corrects":[128],"only":[129],"critical":[130],"errors":[131],"by":[132],"reverting":[133],"previous":[135],"timesteps.":[136],"further":[138],"optimize":[139],"offloading":[140],"intervals":[141],"reorganize":[143],"data":[144],"layouts":[145],"reduce":[147],"memory":[148],"overhead.":[149],"Experiments":[150],"across":[151],"diverse":[152],"models":[153],"datasets":[155],"show":[156],"DRIFT":[158],"can":[159],"achieve":[160],"average":[162],"36%":[163],"savings":[165],"through":[166],"underscaling":[168],"1.7x":[170],"speedup":[171],"via":[172],"overclocking":[173],"while":[174],"maintaining":[175],"quality.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-14T00:00:00"}
