{"id":"https://openalex.org/W7161270657","doi":"https://doi.org/10.48550/arxiv.2605.13869","title":"Elastic Spiking Transformers for Efficient Gesture Understanding","display_name":"Elastic Spiking Transformers for Efficient Gesture Understanding","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7161270657","doi":"https://doi.org/10.48550/arxiv.2605.13869"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13869","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.2605.13869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046416586","display_name":"Alberto Ancilotto","orcid":"https://orcid.org/0000-0002-1396-3709"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ancilotto, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082917039","display_name":"Gianluca Amprimo","orcid":"https://orcid.org/0000-0003-4061-8211"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amprimo, Gianluca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136224959","display_name":"Stefano Di Carlo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Carlo, Stefano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068548938","display_name":"Elisabetta Farella","orcid":"https://orcid.org/0000-0001-9047-9868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farella, Elisabetta","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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9606999754905701,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9606999754905701,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.025200000032782555,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.006099999882280827,"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/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8050000071525574},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6876000165939331},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.6355000138282776},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.59170001745224},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5360000133514404},{"id":"https://openalex.org/keywords/elasticity","display_name":"Elasticity (physics)","score":0.4970000088214874},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43540000915527344},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.3977000117301941},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8050000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470999956130981},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6876000165939331},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.6355000138282776},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5360000133514404},{"id":"https://openalex.org/C121854251","wikidata":"https://www.wikidata.org/wiki/Q62932","display_name":"Elasticity (physics)","level":2,"score":0.4970000088214874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44699999690055847},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.36250001192092896},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.33719998598098755},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3248000144958496},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C50942859","wikidata":"https://www.wikidata.org/wiki/Q4967193","display_name":"Rectification","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2574999928474426},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13869","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.2605.13869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13869","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spiking":[0,5,64,89,183],"Neural":[1],"Networks":[2],"(SNNs),":[3],"particularly":[4],"Transformers,":[6],"offer":[7],"energy-efficient":[8],"processing":[9],"of":[10,189],"event-based":[11],"sensor":[12],"data":[13],"for":[14,58,121],"healthcare":[15],"applications.":[16],"Yet":[17],"current":[18],"architectures":[19],"are":[20,23],"rigid:":[21],"they":[22],"trained":[24,196],"and":[25,34,46,91,107,171],"deployed":[26],"as":[27,44],"static":[28],"networks":[29],"with":[30],"fixed":[31],"parameter":[32,131],"counts":[33],"computational":[35],"graphs.":[36],"This":[37,115],"limits":[38],"deployment":[39],"on":[40,168,204],"neuromorphic":[41],"hardware":[42,135],"such":[43],"Loihi":[45],"SpiNNaker,":[47],"where":[48],"on-chip":[49],"constraints":[50],"often":[51],"require":[52],"smaller":[53],"models":[54],"that":[55,69,180],"trade":[56],"accuracy":[57],"feasibility.":[59],"We":[60,164],"introduce":[61],"the":[62,73,86,126,166,172],"Elastic":[63,182],"Transformer,":[65],"a":[66,98,186],"runtime-adaptive":[67],"architecture":[68],"brings":[70],"elasticity":[71,84],"into":[72],"spiking":[74],"paradigm.":[75],"Inspired":[76],"by":[77],"Matryoshka-style":[78],"representation":[79],"learning,":[80],"it":[81,124],"embeds":[82],"nested":[83],"in":[85,150,159],"Feature":[87],"Extractor,":[88],"Self-Attention,":[90],"Feed-Forward":[92],"blocks.":[93],"Through":[94],"granularity-aware":[95],"weight":[96],"sharing,":[97],"single":[99],"universal":[100],"model":[101,127],"can":[102],"dynamically":[103],"slice":[104],"network":[105],"width":[106],"attention":[108],"heads":[109],"at":[110],"inference":[111],"time":[112],"without":[113],"retraining.":[114],"design":[116],"provides":[117],"two":[118],"key":[119],"advantages":[120],"SNNs.":[122],"First,":[123],"allows":[125],"to":[128,133],"adjust":[129],"its":[130],"footprint":[132],"different":[134],"memory":[136],"budgets.":[137],"Second,":[138],"reducing":[139],"active":[140],"neurons":[141],"also":[142],"lowers":[143],"spike":[144],"firing":[145],"rates,":[146],"yielding":[147],"proportional":[148],"reductions":[149],"synaptic":[151],"operations,":[152],"an":[153],"energy":[154],"benefit":[155],"not":[156],"directly":[157],"available":[158],"standard":[160],"artificial":[161],"neural":[162],"networks.":[163],"evaluate":[165],"approach":[167],"CIFAR10/100,":[169],"CIFAR10-DVS,":[170],"EHWGesture":[173],"clinical":[174],"gesture":[175,202],"understanding":[176],"dataset.":[177],"Results":[178],"show":[179],"one":[181],"Transformer":[184],"spans":[185],"broad":[187],"range":[188],"complexity-accuracy":[190],"trade-offs,":[191],"matching":[192],"or":[193],"surpassing":[194],"independently":[195],"baselines":[197],"while":[198],"supporting":[199],"adaptive,":[200],"real-time":[201],"recognition":[203],"resource-constrained":[205],"edge":[206],"devices.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}