dc.contributor.author | Immonen, Riku | |
dc.contributor.author | Hämäläinen, Timo | |
dc.date.accessioned | 2022-11-15T09:50:32Z | |
dc.date.available | 2022-11-15T09:50:32Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Immonen, R., & Hämäläinen, T. (2022). Tiny Machine Learning for Resource-Constrained Microcontrollers. <i>Journal of Sensors</i>, <i>2022</i>, Article 7437023. <a href="https://doi.org/10.1155/2022/7437023" target="_blank">https://doi.org/10.1155/2022/7437023</a> | |
dc.identifier.other | CONVID_159524299 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/83898 | |
dc.description.abstract | We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed. These are exactly the same resources that play a key role in teaching and running a machine learning model with a basic computer. However, in a microcontroller environment, constrained resources make a critical difference. Therefore, a new paradigm known as tiny machine learning had to be created to meet the constrained requirements of the embedded devices. In this review, we discuss the resource optimization challenges of tiny machine learning and different methods, such as quantization, pruning, and clustering, that can be used to overcome these resource difficulties. Furthermore, we summarize the present state of tiny machine learning frameworks, libraries, development environments, and tools. The benchmarking of tiny machine learning devices is another thing to be concerned about; these same constraints of the microcontrollers and diversity of hardware and software turn to benchmark challenges that must be resolved before it is possible to measure performance differences reliably between embedded devices. We also discuss emerging techniques and approaches to boost and expand the tiny machine learning process and improve data privacy and security. In the end, we form a conclusion about tiny machine learning and its future development. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Hindawi Limited | |
dc.relation.ispartofseries | Journal of Sensors | |
dc.rights | CC BY 4.0 | |
dc.title | Tiny Machine Learning for Resource-Constrained Microcontrollers | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202211155195 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | fi |
dc.contributor.oppiaine | Tekniikka | fi |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | en |
dc.contributor.oppiaine | Engineering | en |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_dcae04bc | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 1687-725X | |
dc.relation.volume | 2022 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2022 Riku Immonen and Timo Hämäläinen. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.grantnumber | A77973 | |
dc.relation.grantnumber | A75239 | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | resurssit | |
dc.subject.yso | esineiden internet | |
dc.subject.yso | sulautettu tietotekniikka | |
dc.subject.yso | reunalaskenta | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p19352 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p27206 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5461 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p39139 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1155/2022/7437023 | |
dc.relation.funder | Council of Tampere Region | en |
dc.relation.funder | Council of Tampere Region | en |
dc.relation.funder | Pirkanmaan liitto | fi |
dc.relation.funder | Pirkanmaan liitto | fi |
jyx.fundingprogram | ERDF European Regional Development Fund, React-EU | en |
jyx.fundingprogram | ERDF European Regional Development Fund, React-EU | en |
jyx.fundingprogram | EAKR Euroopan aluekehitysrahasto, React-EU | fi |
jyx.fundingprogram | EAKR Euroopan aluekehitysrahasto, React-EU | fi |
jyx.fundinginformation | This work has been done under the eÄlytelli and coADDVA funded by the European Regional Development Fund and the Regional Council of Central Finland. | |
dc.type.okm | A2 | |