Research Article
Machine Learning on Standard Embedded Device
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 185 - Issue 19 |
| Published: Jun 2023 |
| Authors: Umapriya Selvam, P. Muthu Subramanian, A. Rajeswari |
10.5120/ijca2023922911
|
Umapriya Selvam, P. Muthu Subramanian, A. Rajeswari . Machine Learning on Standard Embedded Device. International Journal of Computer Applications. 185, 19 (Jun 2023), 8-10. DOI=10.5120/ijca2023922911
@article{ 10.5120/ijca2023922911,
author = { Umapriya Selvam,P. Muthu Subramanian,A. Rajeswari },
title = { Machine Learning on Standard Embedded Device },
journal = { International Journal of Computer Applications },
year = { 2023 },
volume = { 185 },
number = { 19 },
pages = { 8-10 },
doi = { 10.5120/ijca2023922911 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2023
%A Umapriya Selvam
%A P. Muthu Subramanian
%A A. Rajeswari
%T Machine Learning on Standard Embedded Device%T
%J International Journal of Computer Applications
%V 185
%N 19
%P 8-10
%R 10.5120/ijca2023922911
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Developers of ARM microcontrollers now have access to the first neural network software development tools, making machine learning in embedded systems a possibility. This study examines the application of one such tool, the STM Cube AI, on popular ARM Cortex-M microcontrollers. It evaluates and contrasts its performance with that of two others widely employed supervised machine learning (ML) algorithms, namely Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The outcomes of three datasets demonstrate that X-Cube-AI consistently delivers good performance despite the shortcomings of the embedded platform. Popular desktop programs like TensorFlow and Keras are seamlessly incorporated into the workflow.
References
- W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu (2016) “Edge Computing: Vision and Challenges”, in IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646.
- A. Parodi, F. Bellotti, R. Berta, A. De Gloria (2018), “Developing a Machine Learning Library for Microcontrollers” Springer Lecture Notes in Electrical Engineering, vol 550.
- L. Andrade, A. Prost-Boucle, and F. Pétrot, Overview of the state of the art in embedded machine learning, (2018), “ Design, Automation & Test” in Europe Conference & Exhibition (DATE), Dresden, pp. 1033-1038.
- L. Lai and N. Suda (2018), "Enabling Deep Learning at the LoT Edge," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, pp. 1-6.
- G. Cerutti, R. Prasad and E. Farella (2019), "Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 7610-7614.
- M. J. Islam, Q. M. J. Wu, M. Ahmadi, and M. A. Sid-Ahmed, (2007), “Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers”, Int.l Conf. on Convergence Information Technology , Gyeongju, pp. 1541-1546.
Index Terms
Keywords