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Machine Learning on Standard Embedded Device
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 19 |
| Year of Publication: 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
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.
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