{"id":"https://openalex.org/W7165628411","doi":"https://doi.org/10.48550/arxiv.2606.23001","title":"EnerInfer: Energy-Aware On-Device LLM Inference","display_name":"EnerInfer: Energy-Aware On-Device LLM Inference","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165628411","doi":"https://doi.org/10.48550/arxiv.2606.23001"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23001","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23001","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":null,"license_id":null,"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.2606.23001","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035129787","display_name":"Bohua Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Bohua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139133300","display_name":"Nian Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Nian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374253","display_name":"Bo Sun","orcid":"https://orcid.org/0000-0001-7001-8781"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Binqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139179870","display_name":"Matteo Mascherin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mascherin, Matteo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102800785","display_name":"Debayan Roy","orcid":"https://orcid.org/0000-0002-2069-210X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Debayan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139178890","display_name":"Yutao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yutao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139160562","display_name":"Yu Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139181377","display_name":"Ning Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Ning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139152879","display_name":"Haibo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Haibo","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/T12238","display_name":"Green IT and Sustainability","score":0.3402999937534332,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.3402999937534332,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.12139999866485596,"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":0.09889999777078629,"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/inference","display_name":"Inference","score":0.8109999895095825},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5659999847412109},{"id":"https://openalex.org/keywords/profiling","display_name":"Profiling (computer programming)","score":0.47870001196861267},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4458000063896179},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.3711000084877014},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.3522999882698059},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3292999863624573},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.328000009059906}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8109999895095825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7053999900817871},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4458000063896179},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C56685638","wikidata":"https://www.wikidata.org/wiki/Q2300474","display_name":"Power control","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C107861326","wikidata":"https://www.wikidata.org/wiki/Q209233","display_name":"Thermal energy","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C204530211","wikidata":"https://www.wikidata.org/wiki/Q752823","display_name":"Thermal","level":2,"score":0.2671999931335449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23001","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23001","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23001","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23001","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.9074357151985168,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"On-device":[0],"LLM":[1,42,125,138],"inference":[2,43,84,126],"is":[3,34,73],"increasingly":[4],"attractive":[5],"for":[6,26,137,160],"privacy-preserving,":[7],"reliable,":[8],"and":[9,15,52,65,87,103,112,134,144,151,158,174,185,204,210],"cost-effective":[10],"deployment,":[11],"yet":[12],"its":[13],"energy":[14,63,131,197],"thermal":[16,113,135,178],"costs":[17],"remain":[18],"a":[19,208,211],"critical":[20],"bottleneck.":[21],"Existing":[22],"systems":[23],"primarily":[24],"optimize":[25],"decoding":[27],"speed,":[28],"implicitly":[29],"assuming":[30],"that":[31,40,128,194],"faster":[32],"execution":[33],"always":[35],"preferable.":[36],"We":[37],"show":[38,193],"instead":[39],"on-device":[41,124],"often":[44],"has":[45],"exploitable":[46],"configuration":[47],"slack:":[48],"modestly":[49],"lowering":[50],"NPU":[51],"memory":[53],"frequencies":[54],"preserves":[55],"quality":[56],"of":[57],"experience":[58],"(QoE)":[59],"while":[60],"substantially":[61],"improving":[62],"efficiency":[64,198],"reducing":[66],"heat.":[67],"Realizing":[68],"this":[69],"opportunity":[70],"in":[71],"production":[72],"challenging.":[74],"The":[75],"most":[76],"energy-efficient":[77],"NPU/DDR":[78,164],"setting":[79],"varies":[80],"with":[81,90,107,147],"the":[82,122],"model,":[83],"engine,":[85],"platform,":[86],"runtime":[88,172],"conditions,":[89],"no":[91],"stable":[92],"ranking":[93],"across":[94,163],"configurations.":[95],"Commercial":[96],"devices":[97],"further":[98],"lack":[99],"component-level":[100],"power":[101,159],"sensing,":[102],"shell":[104],"temperature":[105],"evolves":[106],"request":[108],"arrivals,":[109],"response":[110],"lengths,":[111],"history.":[114],"To":[115],"address":[116],"these":[117],"challenges,":[118],"we":[119],"propose":[120],"EnerInfer,":[121],"first":[123],"framework":[127],"jointly":[129],"manages":[130],"efficiency,":[132],"throughput,":[133],"comfort":[136],"workloads.":[139],"EnerInfer":[140,195],"replaces":[141],"per-model":[142],"profiling":[143],"sensor-heavy":[145],"control":[146],"disaggregated,":[148],"model-structure-aware":[149],"prediction":[150,179],"ranking-driven":[152],"online":[153],"feedback.":[154],"It":[155],"predicts":[156],"throughput":[157],"unseen":[161],"LLMs":[162,192],"frequency":[165],"settings,":[166],"selects":[167],"QoE-satisfying":[168],"efficient":[169],"configurations":[170],"under":[171],"interference,":[173],"uses":[175],"lightweight":[176],"limited-horizon":[177],"to":[180,201],"dynamically":[181],"switch":[182],"between":[183],"energy-optimized":[184],"thermally":[186],"constrained":[187],"inference.":[188],"Evaluations":[189],"on":[190,206],"real-world":[191],"improves":[196],"by":[199],"up":[200],"65%,":[202],"12%,":[203],"24%":[205],"phones,":[207],"laptop,":[209],"development":[212],"board,":[213],"respectively,":[214],"without":[215],"QoE":[216],"violation.":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}