{"id":"https://openalex.org/W7162341628","doi":"https://doi.org/10.48550/arxiv.2605.23796","title":"UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy","display_name":"UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162341628","doi":"https://doi.org/10.48550/arxiv.2605.23796"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23796","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.2605.23796","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100899640","display_name":"Qinghui Xing","orcid":"https://orcid.org/0000-0003-1953-4574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing, Qinghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136968977","display_name":"Zhuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136990832","display_name":"Xin Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017185276","display_name":"Ouwen Jin","orcid":"https://orcid.org/0000-0001-5687-2156"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Ouwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136922345","display_name":"Ming Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136917017","display_name":"Pan Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Pan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136921922","display_name":"Ying Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055284175","display_name":"Shuiguang Deng","orcid":"https://orcid.org/0000-0001-5015-6095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Shuiguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136982355","display_name":"Gang Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Gang","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":0.9890999794006348,"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":0.9890999794006348,"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":0.002300000051036477,"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":0.0008999999845400453,"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":0.9448999762535095},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.8378999829292297},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6588000059127808},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.600600004196167},{"id":"https://openalex.org/keywords/payload","display_name":"Payload (computing)","score":0.5902000069618225},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.5432999730110168},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.5338000059127808},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5328999757766724}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.9448999762535095},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.8378999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8116000294685364},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.600600004196167},{"id":"https://openalex.org/C134066672","wikidata":"https://www.wikidata.org/wiki/Q1424639","display_name":"Payload (computing)","level":3,"score":0.5902000069618225},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.5432999730110168},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5338000059127808},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5328999757766724},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36489999294281006},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3353999853134155},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3310999870300293},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.33009999990463257},{"id":"https://openalex.org/C113508815","wikidata":"https://www.wikidata.org/wiki/Q193446","display_name":"Packet switching","level":3,"score":0.3061000108718872},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C204673680","wikidata":"https://www.wikidata.org/wiki/Q1628107","display_name":"Airfield traffic pattern","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C2909946758","wikidata":"https://www.wikidata.org/wiki/Q194277","display_name":"Spike train","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23796","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.2605.23796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23796","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":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9135711193084717}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many-core":[0],"neuromorphic":[1],"systems":[2],"accelerate":[3],"Spiking":[4],"Neural":[5],"Networks":[6],"(SNNs),":[7],"yet":[8],"their":[9],"packet-based":[10],"spike":[11,32,74],"communication":[12],"can":[13],"spend":[14],"substantial":[15],"traffic":[16,91],"and":[17,81,99],"energy":[18,101],"repeatedly":[19],"transmitting":[20],"destination":[21],"addresses.":[22],"This":[23,49],"overhead":[24],"is":[25],"amplified":[26],"by":[27,60,92],"the":[28,46,65],"small":[29],"payload":[30],"of":[31,45],"packets:":[33],"in":[34],"representative":[35],"workloads,":[36,88],"duplicate":[37],"address":[38,58],"transmissions":[39],"account":[40],"for":[41,64],"up":[42],"to":[43],"49%":[44],"total":[47],"traffic.":[48],"paper":[50],"presents":[51],"UniSpike,":[52],"a":[53],"hardware-software":[54],"co-design":[55],"that":[56],"removes":[57],"redundancy":[59],"aggregating":[61],"spikes":[62],"destined":[63],"same":[66],"core":[67],"into":[68],"compact":[69],"packets.":[70],"UniSpike":[71,89],"combines":[72],"destination-centric":[73],"scheduling,":[75],"lightweight":[76],"runtime":[77],"packet":[78],"assembly":[79],"hardware,":[80],"destination-aware":[82],"SNN":[83,87],"partitioning.":[84],"Across":[85],"diverse":[86],"reduces":[90],"1.93$\\times$":[93],"on":[94],"average,":[95],"delivering":[96],"1.77$\\times$":[97],"speedup":[98],"1.50$\\times$":[100],"efficiency":[102],"improvement":[103],"over":[104],"state-of-the-art":[105],"designs.":[106]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}