{"id":"https://openalex.org/W4415088406","doi":"https://doi.org/10.1145/3787109.3816400","title":"Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs","display_name":"Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W4415088406","doi":"https://doi.org/10.1145/3787109.3816400"},"language":"en","primary_location":{"id":"doi:10.1145/3787109.3816400","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3816400","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3787109.3816400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119958875","display_name":"Amir Taherin","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amir Taherin","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3646-0901","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100512886","display_name":"Juyi Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juyi Lin","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-2934-5067","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111090558","display_name":"Arash Akbari","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arash Akbari","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0009-0006-1846-0672","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101277741","display_name":"Arman Akbari","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arman Akbari","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0009-0009-6835-030X","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073885088","display_name":"Pu Zhao","orcid":"https://orcid.org/0000-0001-5018-2859"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pu Zhao","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5018-2859","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108139469","display_name":"Weiwei Chen","orcid":"https://orcid.org/0000-0003-0218-2023"},"institutions":[{"id":"https://openalex.org/I4210109081","display_name":"Embody (France)","ror":"https://ror.org/01sy9dm60","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210109081"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Weiwei Chen","raw_affiliation_strings":["EmbodyX Inc., Belmont, CA, USA"],"raw_orcid":"https://orcid.org/0009-0009-8154-917X","affiliations":[{"raw_affiliation_string":"EmbodyX Inc., Belmont, CA, USA","institution_ids":["https://openalex.org/I4210109081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061128237","display_name":"David Kaeli","orcid":"https://orcid.org/0000-0002-5692-0151"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Kaeli","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5692-0151","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651392","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0009-0009-3924-6466"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3024-7990","affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0067599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"234","last_page":"239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13382","display_name":"Robotics and Automated Systems","score":0.9473999738693237,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T13382","display_name":"Robotics and Automated Systems","score":0.9473999738693237,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.7135999798774719},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6201000213623047},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5965999960899353},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.532800018787384},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4884999990463257},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.4505000114440918},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.42899999022483826},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4230000078678131},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4120999872684479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7749999761581421},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.7135999798774719},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6201000213623047},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5965999960899353},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.532800018787384},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.4505000114440918},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4404999911785126},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.42899999022483826},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.38940000534057617},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34929999709129333},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.34439998865127563},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.3206000030040741},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2906000018119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C24057170","wikidata":"https://www.wikidata.org/wiki/Q2497305","display_name":"Gapless playback","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25029999017715454},{"id":"https://openalex.org/C2780615140","wikidata":"https://www.wikidata.org/wiki/Q920419","display_name":"Upgrade","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3787109.3816400","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3816400","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.11480","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.11480","pdf_url":"https://arxiv.org/pdf/2509.11480","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.11480","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.11480","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:10.1145/3787109.3816400","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787109.3816400","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2123620082","display_name":null,"funder_award_id":"CMMI-2402438","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language-Action":[0],"(VLA)":[1],"models":[2],"have":[3],"emerged":[4],"as":[5,22,24,91],"powerful":[6],"generalist":[7],"policies":[8],"for":[9,159],"robotic":[10,160],"control,":[11],"yet":[12],"their":[13,25],"performance":[14,110],"scaling":[15,85],"across":[16,142],"model":[17,95],"architectures":[18],"and":[19,44,50,67,76,94,101,121,139],"hardware":[20,158],"platforms,":[21],"well":[23],"associated":[26],"power":[27,74],"budgets,":[28],"remain":[29],"poorly":[30],"understood.":[31],"This":[32],"work":[33,149],"presents":[34],"an":[35],"evaluation":[36],"of":[37,145,156],"five":[38],"representative":[39],"VLA":[40],"models\u2014spanning":[41],"state-of-the-art":[42],"baselines":[43],"two":[45],"newly":[46],"proposed":[47],"architectures\u2014targeting":[48],"edge":[49,73,106],"datacenter":[51,78,119,157],"GPU":[52,79],"platforms.":[53],"Using":[54],"the":[55,154],"LIBERO":[56],"benchmark,":[57],"we":[58],"measure":[59],"accuracy":[60,130],"alongside":[61],"system-level":[62],"metrics,":[63],"including":[64],"latency,":[65],"throughput,":[66],"peak":[68],"memory":[69,102],"usage,":[70],"under":[71],"varying":[72],"constraints":[75],"high-performance":[77],"configurations.":[80],"Our":[81,148],"results":[82],"identify":[83],"distinct":[84],"trends:":[86],"(1)":[87],"architectural":[88],"choices,":[89],"such":[90],"action":[92],"tokenization":[93],"backbone":[96],"size,":[97],"strongly":[98],"influence":[99],"throughput":[100],"footprint;":[103],"(2)":[104],"power-constrained":[105],"devices":[107],"exhibit":[108],"non-linear":[109],"degradation,":[111],"with":[112],"some":[113],"configurations":[114],"matching":[115],"or":[116],"exceeding":[117],"older":[118],"GPUs;":[120],"(3)":[122],"high-throughput":[123],"variants":[124],"can":[125],"be":[126],"achieved":[127],"without":[128],"significant":[129],"loss.":[131],"These":[132],"findings":[133],"provide":[134],"actionable":[135],"insights":[136],"when":[137],"selecting":[138],"optimizing":[140],"VLAs":[141],"a":[143],"range":[144],"deployment":[146],"constraints.":[147],"challenges":[150],"current":[151],"assumptions":[152],"about":[153],"superiority":[155],"inference.":[161]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-12T00:00:00"}
