{"id":"https://openalex.org/W4413805928","doi":"https://doi.org/10.11113/elektrika.v24n2.555","title":"A Comprehensive Review of Tiny Machine Learning: Enabling AI on Resource-Constrained Devices","display_name":"A Comprehensive Review of Tiny Machine Learning: Enabling AI on Resource-Constrained Devices","publication_year":2025,"publication_date":"2025-08-29","ids":{"openalex":"https://openalex.org/W4413805928","doi":"https://doi.org/10.11113/elektrika.v24n2.555"},"language":"en","primary_location":{"id":"doi:10.11113/elektrika.v24n2.555","doi":"https://doi.org/10.11113/elektrika.v24n2.555","is_oa":true,"landing_page_url":"https://doi.org/10.11113/elektrika.v24n2.555","pdf_url":"https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/download/555/353","source":{"id":"https://openalex.org/S4210175110","display_name":"ELEKTRIKA- Journal of Electrical Engineering","issn_l":"0128-4428","issn":["0128-4428","3083-9394"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ELEKTRIKA- Journal of Electrical Engineering","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/download/555/353","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020601617","display_name":"NURUL ATIKAH BINTI ABBAS","orcid":"https://orcid.org/0000-0002-7803-9971","observed_orcids":["https://orcid.org/0000-0002-7803-9971"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"NURUL ATIKAH BINTI ABBAS","raw_affiliation_strings":["Utm"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Utm","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037942360","display_name":"Mohd Ridzuan Ahmad","orcid":"https://orcid.org/0000-0002-1331-9606","observed_orcids":["https://orcid.org/0000-0002-1331-9606"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"MOHD RIDZUAN BIN AHMAD","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":0.4475,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66266909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"24","issue":"2","first_page":"81","last_page":"97"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.7348999977111816,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.7348999977111816,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.17910000681877136,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13038","display_name":"Internet of Things and AI","score":0.04230000078678131,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/tinyml","display_name":"TinyML","score":0.8360000252723694},{"id":"https://openalex.org/keywords/resource-constrained-devices","display_name":"resource-constrained devices","score":0.9139999747276306},{"id":"https://openalex.org/keywords/edge-computing","display_name":"edge computing","score":0.7929999828338623},{"id":"https://openalex.org/keywords/embedded-systems","display_name":"embedded systems","score":0.7269999980926514},{"id":"https://openalex.org/keywords/low-power-machine-learning","display_name":"low-power machine learning","score":0.7279999852180481},{"id":"https://openalex.org/keywords/on-device-inference","display_name":"on-device inference","score":0.6639999747276306},{"id":"https://openalex.org/keywords/hardware-requirements","display_name":"hardware requirements","score":0.5569999814033508},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.37599998712539673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6301738619804382},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5538250803947449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4239717423915863},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3218657970428467},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11319771409034729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.11113/elektrika.v24n2.555","doi":"https://doi.org/10.11113/elektrika.v24n2.555","is_oa":true,"landing_page_url":"https://doi.org/10.11113/elektrika.v24n2.555","pdf_url":"https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/download/555/353","source":{"id":"https://openalex.org/S4210175110","display_name":"ELEKTRIKA- Journal of Electrical Engineering","issn_l":"0128-4428","issn":["0128-4428","3083-9394"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ELEKTRIKA- Journal of Electrical Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.11113/elektrika.v24n2.555","doi":"https://doi.org/10.11113/elektrika.v24n2.555","is_oa":true,"landing_page_url":"https://doi.org/10.11113/elektrika.v24n2.555","pdf_url":"https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/download/555/353","source":{"id":"https://openalex.org/S4210175110","display_name":"ELEKTRIKA- Journal of Electrical Engineering","issn_l":"0128-4428","issn":["0128-4428","3083-9394"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ELEKTRIKA- Journal of Electrical Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1001717021","display_name":null,"funder_award_id":"J130000","funder_id":"https://openalex.org/F4320323300","funder_display_name":"Universiti Teknologi Malaysia"},{"id":"https://openalex.org/G8700759374","display_name":null,"funder_award_id":"R.J130000","funder_id":"https://openalex.org/F4320323300","funder_display_name":"Universiti Teknologi Malaysia"}],"funders":[{"id":"https://openalex.org/F4320323300","display_name":"Universiti Teknologi Malaysia","ror":"https://ror.org/026w31v75"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413805928.pdf","grobid_xml":"https://content.openalex.org/works/W4413805928.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Recently,":[0],"there":[1],"has":[2],"been":[3],"a":[4,13,82],"growing":[5],"focus":[6,127],"on":[7,12,63,77],"artificial":[8],"intelligence":[9],"(AI),":[10],"particularly":[11],"specific":[14],"type":[15],"known":[16],"as":[17,146],"machine":[18,30,52,60,90,122],"learning.":[19],"This":[20],"technology":[21],"is":[22,104,131],"extensively":[23],"utilized":[24,156],"in":[25,140,157],"smart":[26],"devices.":[27],"However,":[28],"traditional":[29],"learning":[31,61,91,123],"methods":[32],"require":[33],"large":[34],"models":[35],"and":[36,65,85,99,119,153],"substantial":[37],"computing":[38],"power":[39],"to":[40,45,58,71,105,114,132],"operate":[41],"effectively.":[42],"In":[43],"response":[44],"this":[46,129,158],"challenge,":[47],"the":[48,117,137,154],"concept":[49],"of":[50,121,128,136],"\"tiny":[51],"learning\"":[53],"(TinyML)":[54],"emerged.":[55],"TinyML":[56,80],"aims":[57],"enhance":[59],"capabilities":[62],"small":[64],"resource-constrained":[66],"devices":[67],"by":[68],"enabling":[69],"them":[70],"perform":[72],"actions":[73],"locally,":[74],"without":[75],"relying":[76],"cloud-based":[78],"resources.":[79],"represents":[81],"relatively":[83],"new":[84],"exciting":[86],"field":[87],"that":[88,107],"combines":[89],"with":[92],"embedded":[93],"devices,":[94,116],"which":[95],"are":[96,124],"often":[97],"compact":[98],"energy-efficient.":[100],"The":[101,126],"primary":[102],"goal":[103],"ensure":[106],"even":[108],"when":[109],"transitioning":[110],"from":[111],"high-end":[112],"systems":[113],"low-end":[115],"performance":[118],"accuracy":[120],"well-maintained.":[125],"paper":[130],"provide":[133],"an":[134],"overview":[135],"current":[138],"state-of-the-art":[139],"TinyML,":[141],"covering":[142],"various":[143],"aspects":[144],"such":[145],"background":[147],"information,":[148],"software":[149],"implementations,":[150],"hardware":[151],"requirements,":[152],"frameworks":[155],"emerging":[159],"field.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-23T07:36:19.812096","created_date":"2025-10-10T00:00:00"}
