Execution and analysis of classic neural network algorithms when they are implemented in embedded systems

Abstract

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.

IoT-Oriented Design of an Associative Memory Based on Impulsive Hopfield Neural Network with Rate Coding of LIF Oscillators

“…As shown in [32], HN is excellent for processing small amounts of data, having the highest speed of the three compared algorithms. Table 5 represents the execution time of networks algorithms in Arduino UNO [32] and the recognition time of our IHNs without an account of the initiation time τ o . As can be seen, the processing speed of IHNs is comparable to ADALINE, but significantly inferior to the perception module and, especially, to the discrete HN.…”

“…The algorithms of Perceptron, Adaptative Neural Network (ADALINE) and HN are compared and analyzed in work [32], where these are implemented to different development boards of IoT. As shown in [32], HN is excellent for processing small amounts of data, having the highest speed of the three compared algorithms.…”

“…The algorithms of Perceptron, Adaptative Neural Network (ADALINE) and HN are compared and analyzed in work [32], where these are implemented to different development boards of IoT. As shown in [32], HN is excellent for processing small amounts of data, having the highest speed of the three compared algorithms. Table 5 represents the execution time of networks algorithms in Arduino UNO [32] and the recognition time of our IHNs without an account of the initiation time τ o .…”