{"id":"https://openalex.org/W2971489542","doi":"https://doi.org/10.1109/islped.2019.8824897","title":"Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks","display_name":"Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2971489542","doi":"https://doi.org/10.1109/islped.2019.8824897","mag":"2971489542"},"language":"en","primary_location":{"id":"doi:10.1109/islped.2019.8824897","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped.2019.8824897","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075611664","display_name":"Sarada Krithivasan","orcid":"https://orcid.org/0000-0002-2958-1377"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarada Krithivasan","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021284824","display_name":"Sanchari Sen","orcid":"https://orcid.org/0000-0003-0080-2882"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanchari Sen","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010094713","display_name":"Swagath Venkataramani","orcid":"https://orcid.org/0000-0002-0470-6364"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swagath Venkataramani","raw_affiliation_strings":["IBM T.J. Watson Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065766721","display_name":"Anand Raghunathan","orcid":"https://orcid.org/0000-0002-4624-564X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anand Raghunathan","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]}],"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":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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":1.0,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.9258480072021484},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.8446096181869507},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7941097021102905},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6286042332649231},{"id":"https://openalex.org/keywords/bundle","display_name":"Bundle","score":0.6213591694831848},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.6083279848098755},{"id":"https://openalex.org/keywords/synaptic-weight","display_name":"Synaptic weight","score":0.5181083083152771},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4988701343536377},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4579867124557495},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43926504254341125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4298509657382965},{"id":"https://openalex.org/keywords/spike-sorting","display_name":"Spike sorting","score":0.4126593768596649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2864567041397095}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.9258480072021484},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.8446096181869507},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7941097021102905},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6286042332649231},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.6213591694831848},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.6083279848098755},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.5181083083152771},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4988701343536377},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4579867124557495},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43926504254341125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4298509657382965},{"id":"https://openalex.org/C2777613131","wikidata":"https://www.wikidata.org/wiki/Q2003571","display_name":"Spike sorting","level":3,"score":0.4126593768596649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2864567041397095},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped.2019.8824897","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped.2019.8824897","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W101771737","https://openalex.org/W1570411240","https://openalex.org/W2006370340","https://openalex.org/W2123438756","https://openalex.org/W2128222428","https://openalex.org/W2130360162","https://openalex.org/W2138913040","https://openalex.org/W2159951683","https://openalex.org/W2549653513","https://openalex.org/W2612662066","https://openalex.org/W2745005623","https://openalex.org/W2766422095","https://openalex.org/W2775079417","https://openalex.org/W2783525259","https://openalex.org/W2786965798","https://openalex.org/W2800613970","https://openalex.org/W2962811827","https://openalex.org/W2964296416","https://openalex.org/W2964338223","https://openalex.org/W6689892577","https://openalex.org/W6729407136","https://openalex.org/W6745785182","https://openalex.org/W6748763805"],"related_works":["https://openalex.org/W3136143059","https://openalex.org/W3178771700","https://openalex.org/W1968361507","https://openalex.org/W2037099207","https://openalex.org/W3035015922","https://openalex.org/W4399305221","https://openalex.org/W2124872530","https://openalex.org/W2097405940","https://openalex.org/W2366422659","https://openalex.org/W4313442939"],"abstract_inverted_index":{"Spiking":[0],"Neural":[1],"Networks":[2],"(SNNs),":[3],"which":[4],"represent":[5],"information":[6],"as":[7,23,165,188],"sequences":[8],"of":[9,18,37,63,65,105,115],"spikes,":[10],"are":[11,93,147,191],"gaining":[12],"interest":[13],"due":[14],"to":[15,33,59,97,127],"the":[16,60,66,71,75,98,108,113,157,166,180],"emergence":[17],"low-power":[19],"hardware":[20,205],"platforms":[21],"such":[22],"IBM":[24],"TrueNorth":[25],"and":[26,29,87,103,174,221,235],"Intel":[27],"Loihi,":[28],"their":[30],"intrinsic":[31],"ability":[32],"process":[34],"temporal":[35],"streams":[36],"data":[38],"(e.g.,":[39],"outputs":[40],"from":[41,101],"event-based":[42],"cameras).":[43],"A":[44],"spike":[45,159],"produced":[46,138],"by":[47,95,139],"a":[48,140,151],"neuron":[49,141],"in":[50,80,90,145,179,228,233],"an":[51,54,125,200],"SNN":[52,91,202],"is":[53,130,185,243],"event":[55,126,153],"that":[56,142],"triggers":[57],"updates":[58],"membrane":[61],"potentials":[62],"each":[64],"fanout":[67,128],"neurons":[68,129],"based":[69],"on":[70],"weight":[72,168],"associated":[73],"with":[74,150,204,211],"synaptic":[76,99,167],"connection,":[77],"possibly":[78],"resulting":[79],"other":[81],"spikes":[82,106,137,178,210],"being":[83,154],"generated.":[84],"The":[85,182],"time":[86,146],"energy":[88,229],"consumption":[89],"implementations":[92],"dominated":[94],"accesses":[96,164],"weights":[100],"memory":[102,163],"communication":[104,183],"through":[107],"on-chip":[109],"network.":[110],"To":[111],"improve":[112],"energy-efficiency":[114],"SNNs,":[116],"we":[117,197],"therefore":[118],"propose":[119],"Dynamic":[120],"Spike":[121],"Bundling":[122],"(DSB),":[123],"wherein":[124],"not":[131],"generated":[132,155],"for":[133,156,207,230],"every":[134],"spike;":[135],"instead,":[136],"occur":[143],"close":[144],"dynamically":[148,208],"bundled,":[149],"single":[152],"entire":[158],"bundle.":[160,181],"This":[161],"reduces":[162],"can":[169],"be":[170],"fetched":[171],"just":[172],"once":[173],"reused":[175],"across":[176],"all":[177],"traffic":[184],"also":[186],"reduced":[187],"fewer":[189],"messages":[190],"communicated":[192],"between":[193],"neurons.To":[194],"evaluate":[195],"DSB,":[196],"develop":[198],"B-SNNAP,":[199],"event-driven":[201],"accelerator":[203],"support":[206],"bundling":[209],"minimal":[212],"overheads.":[213],"Across":[214],"7":[215],"image":[216],"recognition":[217],"benchmarks":[218],"including":[219],"CIFAR100":[220],"ImageNet":[222],"datasets,":[223],"DSB":[224],"achieves":[225],"1.15\u00d7-3.8\u00d7":[226],"reduction":[227],"<;0.1%":[231],"loss":[232,242],"accuracy,":[234],"upto":[236],"5.1\u00d7":[237],"savings":[238],"when":[239],"<;1%":[240],"accuracy":[241],"tolerable.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}