| Issue |
MATEC Web Conf.
Volume 292, 2019
23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 8 | |
| Section | Circuits and Systems | |
| DOI | https://doi.org/10.1051/matecconf/201929201012 | |
| Published online | 24 September 2019 | |
Execution and analysis of classic neural network algorithms when they are implemented in embedded systems
1 Facultad de Estudios Superiores Cuautitlán, UNAM, ITSE. México.
2 Facultad de Estudios Superiores Cuautitlán, UNAM, Departamento de ingeniería, ITSE. México.
* Corresponding author: dativa19@hotmail.com
Many algorithms related to neural networks are used in a large number of applications, most of them implemented on computational equipment that have great processing and storage capacities, however, new communication schemes such as the Internet of Things, need that neural algorithms can be executed from small electronic devices, devices that do not have large storage or processing capacities, but they can function as intelligent control centres for the different "things" connected to the Internet.
Currently, there are various electronic devices that allow generating low-cost intelligent technology projects that permit interaction within the Internet of things, such as the Arduino UNO, Tiva-C, and BeagleBone development boards. In this project, we present the analysis of the Perceptron, ADALINE and Hopfield neural network algorithms, when they are executed within the three mentioned development boards, in order to define the best tool to be utilized when using such neural schemes and few data are processed. Economic cost, temporary response and technical capabilities of electronic devices have been evaluated.
Key words: Embedded Systems / BeagleBone / Tiva-C LaunchPad / Arduino / Neural Networks / Perceptron / ADALINE / Hopfield / Internet of Things.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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