A2 Katsausartikkeli tieteellisessä aikausilehdessä
Tiny Machine Learning for Resource-Constrained Microcontrollers (2022)

Immonen, R., & Hämäläinen, T. (2022). Tiny Machine Learning for Resource-Constrained Microcontrollers. Journal of Sensors, 2022, Article 7437023. https://doi.org/10.1155/2022/7437023

JYU-tekijät tai -toimittajat

Immonen, Riku
Hämäläinen, Timo

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Immonen, Riku; Hämäläinen, Timo

Lehti tai sarja: Journal of Sensors

ISSN: 1687-725X

eISSN: 1687-7268

Julkaisuvuosi: 2022

Ilmestymispäivä: 10.11.2022

Volyymi: 2022

Artikkelinumero: 7437023

Kustantaja: Hindawi Limited

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1155/2022/7437023

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/83898

Tiivistelmä

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.

YSO-asiasanat: esineiden internet; sulautettu tietotekniikka; reunalaskenta; resurssit; koneoppiminen

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2022

JUFO-taso: 1