Research Article

Machine Learning on Standard Embedded Device

by  Umapriya Selvam, P. Muthu Subramanian, A. Rajeswari
journal cover
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
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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
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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Machine.learning .Artificial..neural..networks Microcontrollers Edge Computing

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