TinyDecisionTreeClassifier: Embedded C++ library for training and applying decision trees on the edge

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00378675" target="_blank" >RIV/68407700:21460/24:00378675 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.softx.2024.101778" target="_blank" >https://doi.org/10.1016/j.softx.2024.101778</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.softx.2024.101778" target="_blank" >10.1016/j.softx.2024.101778</a>

Result language

angličtina

Original language name

TinyDecisionTreeClassifier: Embedded C++ library for training and applying decision trees on the edge

Original language description

Machine learning and AI remain hot topics in research. However, most machine-learning models are trained and applied to big data. Major electronics parts manufacturers are actively working on simplifying the deployment of machine learning models to their chips. Software companies provide microcontroller code generation after the training data are uploaded to their platform. Unfortunately, most of the available open-source solutions do not support training models on the edge. The proposed TinyDecisionTreeClassifier is a standalone open-source C++ library that allows both training and deployment on the edge. This paper also covers the deployment and benchmark of the software performance and power consumption on some of the commonly used microcontrollers.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TM04000062" target="_blank" >TM04000062: Development of the platform for maintaining and monitoring the physical conditions in isolated, confined and extreme environments</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SoftwareX

ISSN

2352-7110

e-ISSN

2352-7110

Volume of the periodical

27

Issue of the periodical within the volume

September

Country of publishing house

AT - AUSTRIA

Number of pages

7

Pages from-to

—

UT code for WoS article

001252828400001

EID of the result in the Scopus database

2-s2.0-85195442432