{"id":"https://openalex.org/W7161249391","doi":"https://doi.org/10.48550/arxiv.2605.13915","title":"Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference","display_name":"Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161249391","doi":"https://doi.org/10.48550/arxiv.2605.13915"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13915","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.2605.13915","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113968407","display_name":"Lingchao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Lingchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136238692","display_name":"Yuwei Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Yuwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136228562","display_name":"Jun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103588449","display_name":"Chengqiu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Chengqiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002244430","display_name":"Qichen Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Qichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051054272","display_name":"Junyi Fan","orcid":"https://orcid.org/0000-0001-5516-7471"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Junyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136226180","display_name":"Rui Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136188665","display_name":"Fangzheng Miao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miao, Fangzheng","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":0.3206999897956848,"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":0.3206999897956848,"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":0.12020000070333481,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.1103999987244606,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/bottleneck","display_name":"Bottleneck","score":0.6682999730110168},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5717999935150146},{"id":"https://openalex.org/keywords/matrix-multiplication","display_name":"Matrix multiplication","score":0.5353000164031982},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.43209999799728394},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.41519999504089355},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.3959999978542328},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.39469999074935913}],"concepts":[{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6682999730110168},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6028000116348267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5800999999046326},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5717999935150146},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.5353000164031982},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.43209999799728394},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.3919999897480011},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38350000977516174},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33079999685287476},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C123213974","wikidata":"https://www.wikidata.org/wiki/Q833089","display_name":"LU decomposition","level":4,"score":0.3073999881744385},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.30239999294281006},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C39096654","wikidata":"https://www.wikidata.org/wiki/Q728507","display_name":"Strassen algorithm","level":4,"score":0.27959999442100525},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13915","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.2605.13915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13915","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Quantization":[0],"is":[1],"essential":[2],"for":[3,19,127,136],"efficient":[4],"large":[5],"language":[6],"model":[7],"(LLM)":[8],"inference,":[9],"yet":[10],"the":[11,47,52,71,109,174,193],"dequantization":[12,40,69,199,231],"step-converting":[13],"low-bit":[14,78],"weights":[15,79,101,140,152],"back":[16],"to":[17,80,115,208,230],"high-precision":[18,85],"matrix":[20,48,216],"multiplication":[21,49,217],"has":[22],"become":[23],"a":[24,63],"critical":[25,73],"bottleneck":[26],"on":[27,215],"modern":[28],"AI":[29],"accelerators.":[30],"On":[31],"architectures":[32],"with":[33,99,162],"decoupled":[34],"compute":[35,178],"units":[36],"(e.g.,":[37],"Ascend":[38],"NPUs),":[39],"operations":[41],"can":[42,95],"consume":[43],"more":[44],"cycles":[45],"than":[46],"itself,":[50],"leaving":[51],"high-throughput":[53],"tensor":[54],"cores":[55],"underutilized.":[56],"This":[57,106],"paper":[58],"presents":[59],"Multi-Scale":[60],"Dequant":[61],"(MSD),":[62],"quantization":[64],"framework":[65],"that":[66,190,223],"removes":[67],"weight/KV":[68],"from":[70,112],"GEMM":[72,177],"path.":[74],"Instead":[75],"of":[76,93],"lifting":[77],"BF16":[81,86],"precision,":[82],"MSD":[83,126,191,224],"decomposes":[84],"activations":[87],"into":[88],"multiple":[89],"low-precision":[90],"components,":[91],"each":[92],"which":[94],"be":[96],"multiplied":[97],"directly":[98],"quantized":[100],"via":[102],"native":[103],"hardware-accelerated":[104],"GEMM.":[105,123],"approach":[107],"shifts":[108],"computational":[110],"paradigm":[111],"precision":[113],"conversion":[114,121],"multi-scale":[116],"approximation,":[117],"avoiding":[118],"INT8-to-BF16":[119],"weight":[120,129],"before":[122],"We":[124,180],"instantiate":[125],"two":[128],"formats":[130],"and":[131,185,200,218,233],"derive":[132,182],"tight":[133],"error":[134,163],"bounds":[135],"each.":[137],"For":[138,150],"INT8":[139,143],"(W4A16),":[141,153],"two-pass":[142,154],"decomposition":[144,156],"achieves":[145,237],"near":[146,158],"16":[147],"effective":[148,160,176],"bits.":[149],"MXFP4":[151,155],"yields":[157],"6.6":[159],"bits":[161],"bound":[164],"1/64":[165],"per":[166],"block":[167],"surpassing":[168],"single-pass":[169],"MXFP8(5.24":[170],"bits)":[171],"while":[172],"maintaining":[173],"same":[175],"time.":[179],"further":[181],"closed-form":[183],"latency":[184],"HBM":[186,204],"traffic":[187,205],"models":[188],"showing":[189],"avoids":[192],"Vector-Cube":[194],"pipeline":[195],"stall":[196],"caused":[197],"by":[198,206],"reduces":[201],"KV":[202],"cache":[203],"up":[207],"2.5":[209],"times":[210],"in":[211,234],"attention.":[212],"Numerical":[213],"simulations":[214],"Flash":[219],"Attention":[220],"kernels":[221],"confirm":[222],"does":[225],"not":[226],"degrade":[227],"accuracy":[228],"compared":[229],"baselines,":[232],"many":[235],"settings":[236],"lower":[238],"L2":[239],"error.":[240]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}
