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On-device training of artificial intelligence models on microcontrollers

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 2829–2839

DOI
10.11591/ijai.v13.i3.pp2829-2839
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.

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19 penulis · 49 tautan kepenulisan bersama · 12 lintas organisasi

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  • Can Tho University
  • Vinh Long University of Technology Education
  • Trường Đại học Kỹ thuật - Công nghệ Cần Thơ
  • Kien Giang University
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Van-Muot NguyenCan Tho University4
Hoang-Dung NguyenCan Tho University4
Chanh-Nghiem NguyenCan Tho University2
The Anh HoTrường Đại học Kỹ thuật - Công nghệ Cần Thơ2
Van Huu BuiCan Tho University2
Tu Dinh NguyenTrường Đại học Kỹ thuật - Công nghệ Cần Thơ2
Van-Linh LamKien Giang University1
Phúc Hậu LêCan Tho University1
Huy-Thanh HoCan Tho University1
Ngọc-Tan VoCan Tho University1
Ngoc-Thanh NguyenCan Tho University1
Nhut-Thanh TranCan Tho University1
Minh-Thanh LeVinh Long University of Technology Education1
Thanh Tung PhamVinh Long University of Technology Education1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">On-device training of artificial intelligence models on microcontrollers</dc:title>
	<dc:creator>Thai, Bao-Toan</dc:creator>
	<dc:creator>Tran, Vy-Khang</dc:creator>
	<dc:creator>Pham, Hai</dc:creator>
	<dc:creator>Nguyen, Chi-Ngon</dc:creator>
	<dc:creator>Nguyen, Van-Khanh</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Free real-time operating system; Micro-controllers; On-device training; Real-time operating system</dc:subject>
	<dc:description xml:lang="en-US">Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24704</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp2829-2839</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2829-2839</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24704/14091</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>