The main aim of the project is to perform machine learning on the AURIX TC297 TFT board (TC29x B-Step MCU). Relying on the board features and its memory and computation constraints, a study has been done to understand which machine learning regression models could be adapted and run on the device.
The essential board components for this purpose are Ethernet, display, and multicore execution. The board communicates with a Python client installed in the host PC through the Ethernet connection, given its reliability and flexibility. The display is the simplest interface to give a quick view of the available data, without waiting for a download and post-processing of the results. The multicore execution is used to increase the computation performance.
Through the Ethernet connection it is possible to send data and receive the predictions from the models implemented within the board. Communication takes place through the classic TCP/IP protocol, where the board acts as a server and responds to connection requests from clients (which in the case study are installed in the Host PC, but can be installed on other boards as well). The scatter-plot of the predictions, which is typically used to show the results of regression models, is also plotted on the board screen during the data acquisition.
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