{"id":"https://openalex.org/W4385301522","doi":"https://doi.org/10.1109/coins57856.2023.10189194","title":"End-to-end Evolutionary Neural Architecture Search for Microcontroller Units","display_name":"End-to-end Evolutionary Neural Architecture Search for Microcontroller Units","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4385301522","doi":"https://doi.org/10.1109/coins57856.2023.10189194"},"language":"en","primary_location":{"id":"doi:10.1109/coins57856.2023.10189194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins57856.2023.10189194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","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/A5083555853","display_name":"Ren\u00e9 Groh","orcid":"https://orcid.org/0000-0002-3405-1311"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ren\u00e9 Groh","raw_affiliation_strings":["Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Dept. of Artificial Intelligence in Biomedical Engineering,Erlangen,Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Dept. of Artificial Intelligence in Biomedical Engineering,Erlangen,Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088428516","display_name":"Andreas M. Kist","orcid":"https://orcid.org/0000-0003-3643-7776"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas M. Kist","raw_affiliation_strings":["Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Dept. of Artificial Intelligence in Biomedical Engineering,Erlangen,Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Dept. of Artificial Intelligence in Biomedical Engineering,Erlangen,Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083555853"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6435289,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9940999746322632,"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"}},"topics":[{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9940999746322632,"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"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9747999906539917,"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/computer-science","display_name":"Computer science","score":0.8324006199836731},{"id":"https://openalex.org/keywords/microcontroller","display_name":"Microcontroller","score":0.5970385074615479},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.554814875125885},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.539631724357605},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.5101357102394104},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4582066535949707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3837098479270935},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.35360804200172424},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.32796838879585266}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8324006199836731},{"id":"https://openalex.org/C173018170","wikidata":"https://www.wikidata.org/wiki/Q165678","display_name":"Microcontroller","level":2,"score":0.5970385074615479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.554814875125885},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.539631724357605},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.5101357102394104},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4582066535949707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3837098479270935},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.35360804200172424},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32796838879585266},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coins57856.2023.10189194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins57856.2023.10189194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323237","display_name":"Bayerisches Staatsministerium f\u00fcr Wissenschaft, Forschung und Kunst","ror":"https://ror.org/01a44gd51"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2010788560","https://openalex.org/W2112299196","https://openalex.org/W2126105956","https://openalex.org/W2186162664","https://openalex.org/W2553303224","https://openalex.org/W2721931776","https://openalex.org/W2769912137","https://openalex.org/W2788853733","https://openalex.org/W2797583228","https://openalex.org/W2799899460","https://openalex.org/W2907271713","https://openalex.org/W2907828870","https://openalex.org/W2942026628","https://openalex.org/W2951104886","https://openalex.org/W2954248940","https://openalex.org/W2963437853","https://openalex.org/W2963918968","https://openalex.org/W2963946985","https://openalex.org/W2964024268","https://openalex.org/W2964052309","https://openalex.org/W2981406437","https://openalex.org/W2995086119","https://openalex.org/W3014603761","https://openalex.org/W3015287265","https://openalex.org/W3020308886","https://openalex.org/W3029332223","https://openalex.org/W3088798822","https://openalex.org/W3103028792","https://openalex.org/W3119913666","https://openalex.org/W3160945783","https://openalex.org/W3171973026","https://openalex.org/W3184493268","https://openalex.org/W4220815313","https://openalex.org/W6740352302","https://openalex.org/W6746451879","https://openalex.org/W6748587240","https://openalex.org/W6752515464","https://openalex.org/W6809337877"],"related_works":["https://openalex.org/W4316095964","https://openalex.org/W2383001583","https://openalex.org/W2771395446","https://openalex.org/W2131084560","https://openalex.org/W3112038843","https://openalex.org/W3209836052","https://openalex.org/W2088310429","https://openalex.org/W2929170389","https://openalex.org/W4300097863","https://openalex.org/W2950268673"],"abstract_inverted_index":{"Smart":[0],"wearable":[1],"devices":[2],"require":[3],"accurate,":[4],"fast,":[5],"and":[6,39,57,98,153,160,181],"energy-efficient":[7],"neural":[8,21,40,44,89,164],"networks":[9],"to":[10,60,66,93,157],"allow":[11],"for":[12,32],"optimal":[13],"application":[14],"performance.":[15],"To":[16,69],"advance":[17],"the":[18,50,78,95,111,147,150,169],"field":[19],"of":[20,73,105,113,149,191],"architecture":[22,46],"search":[23],"(NAS),":[24],"we":[25,81,108],"introduce":[26],"our":[27,142],"end-to-end":[28,143],"evolutionary":[29],"NAS":[30],"(EvoNAS)":[31],"microcontroller":[33,79],"units":[34],"that":[35,120,141],"optimize":[36],"both,":[37],"pre-processing":[38,127],"network":[41,45,90,165],"architectures.":[42],"Each":[43],"is":[47,91,154,172,179],"assessed":[48],"using":[49],"multi-objective":[51],"accuracy,":[52],"memory":[53],"footprint,":[54],"inference":[55,96],"time,":[56],"energy":[58],"consumption,":[59],"derive":[61],"a":[62,83,103,184,188],"common":[63],"performance":[64],"measure":[65,94],"be":[67],"maximized.":[68],"ensure":[70],"immediate":[71],"use":[72,186],"all":[74],"potential":[75],"solutions":[76],"on":[77,110],"environment,":[80],"create":[82],"software-hardware":[84],"chain":[85],"in":[86],"which":[87],"each":[88],"deployed":[92],"time":[97],"power":[99],"consumption":[100],"directly.":[101],"In":[102],"proof":[104],"concept":[106],"study,":[107],"focused":[109],"analysis":[112],"audio-based":[114],"speech":[115],"commands.":[116],"Our":[117,175],"experiments":[118],"suggest":[119],"2D":[121],"convolutional":[122,133],"layers":[123,134],"with":[124,135,146],"automatically":[125],"set":[126],"(short-time":[128],"Fourier":[129],"transforms)":[130],"outperform":[131],"1D":[132],"raw":[136],"audio":[137],"signals.":[138],"We":[139],"show":[140],"EvoNAS":[144,177],"scales":[145],"complexity":[148],"classification":[151,170],"task":[152,171],"still":[155],"able":[156],"find":[158],"constraint-preserving,":[159],"thus":[161],"deployable,":[162],"Pareto-optimal":[163],"architectures":[166],"even":[167],"when":[168],"more":[173],"complex.":[174],"proposed":[176],"approach":[178],"dataset":[180],"hardware-agnostic,":[182],"allowing":[183],"universal":[185],"across":[187],"wide":[189],"range":[190],"applications.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
