{"id":"https://openalex.org/W4396949414","doi":"https://doi.org/10.1109/isqed60706.2024.10528706","title":"Unleashing Energy-Efficiency: Neural Architecture Search without Training for Spiking Neural Networks on Loihi Chip","display_name":"Unleashing Energy-Efficiency: Neural Architecture Search without Training for Spiking Neural Networks on Loihi Chip","publication_year":2024,"publication_date":"2024-04-03","ids":{"openalex":"https://openalex.org/W4396949414","doi":"https://doi.org/10.1109/isqed60706.2024.10528706"},"language":"en","primary_location":{"id":"doi:10.1109/isqed60706.2024.10528706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed60706.2024.10528706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 25th International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"},"type":"article","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/A5101459754","display_name":"Shiya Liu","orcid":"https://orcid.org/0000-0002-2461-0490"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shiya Liu","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computing Engineering,Blacksburg,Virginia,USA,24061"],"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computing Engineering,Blacksburg,Virginia,USA,24061","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018632091","display_name":"Yang Yi","orcid":"https://orcid.org/0000-0002-1354-0204"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Yi","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computing Engineering,Blacksburg,Virginia,USA,24061"],"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computing Engineering,Blacksburg,Virginia,USA,24061","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101459754"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.4614,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60701515,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9994999766349792,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9983999729156494,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6409225463867188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6352701783180237},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.5832663774490356},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5054426193237305},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4808962345123291},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.462909072637558},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.37562260031700134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29194551706314087},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13873904943466187},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05024316906929016}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6409225463867188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6352701783180237},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.5832663774490356},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5054426193237305},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4808962345123291},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.462909072637558},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.37562260031700134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29194551706314087},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13873904943466187},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05024316906929016},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isqed60706.2024.10528706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isqed60706.2024.10528706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 25th International Symposium on Quality Electronic Design (ISQED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"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":50,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1616857247","https://openalex.org/W2107878631","https://openalex.org/W2112661761","https://openalex.org/W2130459697","https://openalex.org/W2521023735","https://openalex.org/W2553303224","https://openalex.org/W2750384547","https://openalex.org/W2783525259","https://openalex.org/W2804736530","https://openalex.org/W2809090039","https://openalex.org/W2892077605","https://openalex.org/W2949257049","https://openalex.org/W2967733054","https://openalex.org/W2970338293","https://openalex.org/W3007867923","https://openalex.org/W3023721287","https://openalex.org/W3030728803","https://openalex.org/W3035000326","https://openalex.org/W3043133474","https://openalex.org/W3102040318","https://openalex.org/W3102750118","https://openalex.org/W3121924028","https://openalex.org/W3126711481","https://openalex.org/W3132101459","https://openalex.org/W3173704789","https://openalex.org/W3186451123","https://openalex.org/W3186962187","https://openalex.org/W3192682950","https://openalex.org/W3214253320","https://openalex.org/W4226471655","https://openalex.org/W4281382357","https://openalex.org/W4295312788","https://openalex.org/W6631190155","https://openalex.org/W6666761814","https://openalex.org/W6727033975","https://openalex.org/W6743688258","https://openalex.org/W6752495264","https://openalex.org/W6752515464","https://openalex.org/W6766330063","https://openalex.org/W6766978945","https://openalex.org/W6771742384","https://openalex.org/W6774680395","https://openalex.org/W6779348065","https://openalex.org/W6788713141","https://openalex.org/W6790089871","https://openalex.org/W6790297421","https://openalex.org/W6793164127","https://openalex.org/W6796973776","https://openalex.org/W6838329916"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W3126544799","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W2038503502","https://openalex.org/W2403181385","https://openalex.org/W4213353724"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,23,73,108],"networks":[2],"(SNNs)":[3],"offer":[4],"energy-efficient":[5,60,119,216],"computation":[6],"due":[7],"to":[8,36,43,104,121,147,196],"their":[9],"high-sparsity":[10],"activation":[11],"and":[12,84,166,175,206],"event-driven":[13,114],"nature.":[14],"However,":[15],"existing":[16],"SNN":[17,50,63,88,190,217],"designs":[18,218],"often":[19,34],"utilize":[20],"suboptimal":[21],"artificial":[22],"network":[24],"(ANN)-like":[25],"architectures":[26,51,89,198],"for":[27,53,91,143,163,215,229],"binary":[28],"sequence":[29],"processing.":[30],"Moreover,":[31],"improving":[32],"accuracy":[33,195],"leads":[35],"higher":[37],"computational":[38],"complexity,":[39],"making":[40,116],"it":[41,117],"difficult":[42],"deploy":[44],"SNNs":[45,228],"on":[46,86,180,219],"resource-constrained":[47],"devices.":[48],"Furthermore,":[49],"tailored":[52],"GPUs":[54],"may":[55],"not":[56],"fully":[57],"exploit":[58],"the":[59,92,106,141,155,167,186,213,220,224],"capabilities":[61],"of":[62,169,188,227],"models.":[64],"To":[65],"address":[66],"these":[67],"limitations,":[68],"we":[69],"present":[70],"a":[71,99,149],"novel":[72],"architecture":[74,129],"search":[75,150],"(NAS)":[76],"algorithm":[77,124],"that":[78,152],"merges":[79],"recent":[80],"advancements":[81],"in":[82,113],"ANNs":[83],"focuses":[85],"enhancing":[87],"specifically":[90],"Loihi":[93,96,221],"chip.":[94],"The":[95],"chip":[97,102],"is":[98],"neuromorphic":[100],"computing":[101],"designed":[103],"emulate":[105],"brain\u2019s":[107],"networks,":[109],"with":[110,154],"particular":[111],"strength":[112],"SNNs,":[115],"an":[118,127],"alternative":[120],"GPUs.":[122],"Our":[123,210],"efficiently":[125],"selects":[126],"optimal":[128],"by":[130],"leveraging":[131],"gradients":[132],"induced":[133],"at":[134],"initialization":[135],"across":[136],"diverse":[137],"data":[138],"samples,":[139],"eliminating":[140],"requirement":[142],"training.":[144],"We":[145],"propose":[146],"design":[148],"space":[151],"aligns":[153],"chip\u2019s":[156],"capabilities,":[157],"taking":[158],"into":[159],"account":[160],"its":[161],"support":[162],"integer-only":[164],"inference":[165],"lack":[168],"advanced":[170],"operators":[171],"such":[172],"as":[173],"backward":[174],"shortcut":[176],"connections.":[177],"Experimental":[178],"results":[179],"two":[181],"image":[182,205],"classification":[183],"benchmarks":[184],"demonstrate":[185],"superiority":[187],"our":[189],"models,":[191],"which":[192],"achieve":[193],"comparable":[194],"state-of-the-art":[197],"while":[199],"significantly":[200],"reducing":[201],"energy":[202],"consumption":[203],"per":[204],"minimizing":[207],"model":[208],"size.":[209],"approach":[211],"paves":[212],"way":[214],"chip,":[222],"unlocking":[223],"full":[225],"potential":[226],"real-world":[230],"applications.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}