{"id":"https://openalex.org/W3215653427","doi":"https://doi.org/10.1109/mascots53633.2021.9614302","title":"Efficient Brain-Inspired Hyperdimensional Learning with Spatiotemporal Structured Data","display_name":"Efficient Brain-Inspired Hyperdimensional Learning with Spatiotemporal Structured Data","publication_year":2021,"publication_date":"2021-11-03","ids":{"openalex":"https://openalex.org/W3215653427","doi":"https://doi.org/10.1109/mascots53633.2021.9614302","mag":"3215653427"},"language":"en","primary_location":{"id":"doi:10.1109/mascots53633.2021.9614302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mascots53633.2021.9614302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)","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/A5102749150","display_name":"Jiseung Kim","orcid":"https://orcid.org/0009-0000-4361-8079"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jiseung Kim","raw_affiliation_strings":["DGIST"],"affiliations":[{"raw_affiliation_string":"DGIST","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004378291","display_name":"Hyunsei Lee","orcid":"https://orcid.org/0009-0009-7780-8167"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunsei Lee","raw_affiliation_strings":["DGIST"],"affiliations":[{"raw_affiliation_string":"DGIST","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033221192","display_name":"Mohsen Imani","orcid":"https://orcid.org/0000-0002-5761-0622"},"institutions":[{"id":"https://openalex.org/I4210137750","display_name":"UC Irvine Health","ror":"https://ror.org/03fgher32","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210137750"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohsen Imani","raw_affiliation_strings":["UC Irvine"],"affiliations":[{"raw_affiliation_string":"UC Irvine","institution_ids":["https://openalex.org/I4210137750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067380102","display_name":"Yeseong Kim","orcid":"https://orcid.org/0000-0001-5947-9632"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yeseong Kim","raw_affiliation_strings":["DGIST"],"affiliations":[{"raw_affiliation_string":"DGIST","institution_ids":["https://openalex.org/I193352282"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102749150"],"corresponding_institution_ids":["https://openalex.org/I193352282"],"apc_list":null,"apc_paid":null,"fwci":0.4011,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.61471178,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9990000128746033,"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":0.9908999800682068,"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/speedup","display_name":"Speedup","score":0.8343362212181091},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8056522011756897},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.783108115196228},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6738157272338867},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.6616212129592896},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.564089298248291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5230939388275146},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46601277589797974},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3383546471595764},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3313305974006653},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.21555644273757935},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19568637013435364}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.8343362212181091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8056522011756897},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.783108115196228},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6738157272338867},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.6616212129592896},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.564089298248291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5230939388275146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46601277589797974},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3383546471595764},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3313305974006653},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.21555644273757935},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19568637013435364},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/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/mascots53633.2021.9614302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mascots53633.2021.9614302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1129461654","https://openalex.org/W1573706465","https://openalex.org/W1993533483","https://openalex.org/W2069186336","https://openalex.org/W2070862086","https://openalex.org/W2112796928","https://openalex.org/W2583857261","https://openalex.org/W2613567208","https://openalex.org/W2750384547","https://openalex.org/W2771100829","https://openalex.org/W2798651459","https://openalex.org/W2897044384","https://openalex.org/W2905304202","https://openalex.org/W2946584982","https://openalex.org/W2963782760","https://openalex.org/W2964024268","https://openalex.org/W2967927776","https://openalex.org/W2970402754","https://openalex.org/W2974428947","https://openalex.org/W3032819016","https://openalex.org/W3034923703","https://openalex.org/W3036478084","https://openalex.org/W3103415979","https://openalex.org/W3106455851","https://openalex.org/W3158860039","https://openalex.org/W3184049905","https://openalex.org/W3186989076","https://openalex.org/W3208591904","https://openalex.org/W3212827699","https://openalex.org/W3212965658","https://openalex.org/W3213820542","https://openalex.org/W6627360898","https://openalex.org/W6743688258","https://openalex.org/W6748135177"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W2097707447"],"abstract_inverted_index":{"Brain-inspired":[0],"hyperdimensional":[1],"(HD)":[2],"computing":[3,7],"is":[4],"a":[5,28,52,69,99],"new":[6],"paradigm":[8],"based":[9],"on":[10,118],"theoretical":[11],"neuroscience":[12],"to":[13,25,31],"enable":[14],"efficient":[15],"learning.":[16],"In":[17,37],"HD":[18],"computing,":[19],"the":[20,76,90,108,113],"original":[21],"data":[22,49,67],"are":[23],"encoded":[24],"points":[26],"in":[27],"high-dimensional":[29],"space":[30],"perform":[32],"learning":[33],"with":[34,51,98],"lightweight":[35],"algebra.":[36],"this":[38],"paper,":[39],"we":[40,111],"propose":[41],"STEMHD":[42,63,83],"that":[43,55,62,82],"elicits":[44],"key":[45],"features":[46],"from":[47],"spatiotemporal":[48],"along":[50],"hardware":[53],"design":[54],"empowers":[56],"computation":[57],"reuse.":[58],"Our":[59,79],"evaluation":[60,80],"shows":[61,81],"successfully":[64],"interprets":[65],"structural":[66],"at":[68],"low":[70],"cost":[71],"achieving":[72],"higher":[73],"accuracy":[74,101],"than":[75,105],"state-of-the-art":[77],"methods.":[78],"improves":[84],"performance":[85],"and":[86,95],"energy":[87],"efficiency":[88],"during":[89],"model":[91,109],"training":[92],"by":[93],"16.3%":[94],"19.7%,":[96],"respectively,":[97],"negligible":[100],"loss":[102],"of":[103,116],"less":[104],"0.25%.":[106],"For":[107],"inference,":[110],"observe":[112],"inference":[114],"speedup":[115],"1.96\u00d7":[117],"average.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}