Uporaba strojnega učenja na vgrajenih platformah

KLANČNIK, BIAN (author), Kukar, Matjaž (mentor)

V zadnjem desetletju se je strojno učenje precej razvilo in prodira na vsa področja informacijskih tehnologij. Dandanes večina računalniških sistemov uporablja strojno učenje na tak ali drugačen način. Poleg tega pa je zelo napredovalo tudi strojno učenje na manj zmogljivih napravah. Cilj diplomske naloge je preizkusiti učinkovitost obstoječih orodij za strojno učenje na manj zmogljivih napravah. Osredotočili smo se na naprave ARM. Na osebnem računalniku smo zgradili več različnih modelov v različnih ogrodjih za grajenje modelov strojnega učenja. Modele smo serializirali s pomočjo orodij za serializacijo in jih na koncu pognali na Raspberry Pi. Zgradili smo več klasifikacijskih in en regresijski model. Merili smo uspešnost modelov in čas, ki ga model na določeni napravi porabi za napovedovanje. Rezultati so pokazali, da se uspešnost modelov na različnih napravah ne razlikuje. Razlika v izmerjenem času pa se je med napravami precej razlikovala.

• Language: Slovenian
• Keywords: strojno učenje, vgrajene naprave, serializacija modelov
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2021
• PID: 20.500.12556/RUL-130332
• COBISS.SI-ID: 77621507
• Publication date in RUL: 13.09.2021

KLANČNIK, BIAN, 2021, Uporaba strojnega učenja na vgrajenih platformah [online]. Bachelor’s thesis. [Accessed 1 April 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=130332

Secondary language

• Language: English
• Title: Machine learning on embedded platforms

Abstract

Machine learning has developed considerably in the last decade and is penetrating all areas of information technology. Today, most computer systems use machine learning in one way or another. In addition, machine learning on less powerful devices has advanced greatly. The aim of the diploma thesis is to test the effectiveness of existing machine learning tools on less powerful devices. We focused on ARM devices. We built several different models on a personal computer in different frameworks to build machine learning models. We serialized the models using serialization tools and eventually ran them on a Raspberry Pi. We built several classification and one regression model. We measured the performance of the models and the time that the model spends on a particular device to predict. The results showed that the performance of the models on different devices did not differ. The difference in measured time, however, varied considerably between devices.

• Keywords: machine learning, embedded devices, model serialization