{"id":"https://openalex.org/W4391766381","doi":"https://doi.org/10.48550/arxiv.2402.06004","title":"Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy","display_name":"Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy","publication_year":2024,"publication_date":"2024-02-08","ids":{"openalex":"https://openalex.org/W4391766381","doi":"https://doi.org/10.48550/arxiv.2402.06004"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2402.06004","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.06004","pdf_url":"https://arxiv.org/pdf/2402.06004","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.06004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104209623","display_name":"Seyedarmin Azizi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azizi, Seyedarmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065028689","display_name":"Mahdi Nazemi","orcid":"https://orcid.org/0000-0003-4731-3568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nazemi, Mahdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5044650311","display_name":"Massoud Pedram","orcid":"https://orcid.org/0000-0002-2677-7307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pedram, Massoud","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":true,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.996999979019165,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.996999979019165,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9854999780654907,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9750999808311462,"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/rank","display_name":"Rank (graph theory)","score":0.5459514856338501},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5448139309883118},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4505199193954468},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.36081892251968384},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3430803418159485},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33215174078941345},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20384198427200317},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.17125436663627625},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14324259757995605},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08720287680625916}],"concepts":[{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5459514856338501},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5448139309883118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4505199193954468},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.36081892251968384},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3430803418159485},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33215174078941345},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20384198427200317},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.17125436663627625},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14324259757995605},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08720287680625916},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2402.06004","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.06004","pdf_url":"https://arxiv.org/pdf/2402.06004","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2402.06004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.06004","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:arXiv.org:2402.06004","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.06004","pdf_url":"https://arxiv.org/pdf/2402.06004","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391766381.pdf","grobid_xml":"https://content.openalex.org/works/W4391766381.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2152992791","https://openalex.org/W2931662336","https://openalex.org/W2077865380","https://openalex.org/W2134894512","https://openalex.org/W2053487507","https://openalex.org/W2765597752","https://openalex.org/W2083375246","https://openalex.org/W2067108088","https://openalex.org/W2085372204"],"abstract_inverted_index":{"As":[0],"Vision":[1],"Transformers":[2],"(ViTs)":[3],"increasingly":[4],"set":[5],"new":[6],"benchmarks":[7],"in":[8,139,145,190,239],"computer":[9],"vision,":[10],"their":[11,21,244],"practical":[12],"deployment":[13],"on":[14,180],"inference":[15],"engines":[16],"is":[17,63,106],"often":[18],"hindered":[19],"by":[20,36,109,172],"significant":[22],"memory":[23,27,34],"bandwidth":[24],"and":[25,92,149,158],"(on-chip)":[26],"footprint":[28],"requirements.":[29],"This":[30,104],"paper":[31],"addresses":[32],"this":[33],"limitation":[35],"introducing":[37],"an":[38,111],"activation-aware":[39],"model":[40,156,211],"compression":[41,157,199],"methodology":[42],"that":[43,117],"uses":[44,118],"selective":[45],"low-rank":[46,191],"weight":[47,67,90],"tensor":[48,91,102],"approximations":[49],"of":[50,58,72,83,95,121,128,170,229],"different":[51],"layers":[52],"to":[53,64,195,206,220],"reduce":[54],"the":[55,66,78,81,84,88,93,96,100,119,122,146,155,162,167,181,185,197,209,227],"parameter":[56,168],"count":[57,169],"ViTs.":[59],"The":[60,126],"key":[61],"idea":[62],"decompose":[65],"tensors":[68,75],"into":[69],"a":[70,140,151,233],"sum":[71],"two":[73],"parameter-efficient":[74],"while":[76,134,217],"minimizing":[77],"error":[79,114],"between":[80,154],"product":[82,94],"input":[85,97],"activations":[86,98],"with":[87,99,174],"original":[89],"approximate":[101],"sum.":[103],"approximation":[105],"further":[107],"refined":[108],"adopting":[110],"efficient":[112],"layer-wise":[113],"compensation":[115],"technique":[116,200],"gradient":[120],"layer's":[123],"output":[124,159],"loss.":[125],"combination":[127],"these":[129],"techniques":[130],"achieves":[131],"excellent":[132],"results":[133,225],"it":[135],"avoids":[136],"being":[137],"trapped":[138],"shallow":[141],"local":[142],"minimum":[143],"early":[144],"optimization":[147],"process":[148],"strikes":[150],"good":[152],"balance":[153],"accuracy.":[160],"Notably,":[161],"presented":[163,198],"method":[164],"significantly":[165],"reduces":[166],"DeiT-B":[171],"60%":[173],"less":[175],"than":[176],"1%":[177],"accuracy":[178,187,222],"drop":[179],"ImageNet":[182],"dataset,":[183],"overcoming":[184],"usual":[186],"degradation":[188],"seen":[189],"approximations.":[192],"In":[193],"addition":[194],"this,":[196],"can":[201],"compress":[202],"large":[203],"DeiT/ViT":[204,215],"models":[205],"have":[207],"about":[208],"same":[210],"size":[212],"as":[213],"smaller":[214],"variants":[216],"yielding":[218],"up":[219],"1.8%":[221],"gain.":[223],"These":[224],"highlight":[226],"efficacy":[228],"our":[230],"approach,":[231],"presenting":[232],"viable":[234],"solution":[235],"for":[236],"embedding":[237],"ViTs":[238],"memory-constrained":[240],"environments":[241],"without":[242],"compromising":[243],"performance.":[245]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2024-02-13T00:00:00"}