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* All the files required to run the test—the executable, the image files, etc.—are stored on a tmpfs RAM disk in order to make file system/storage medium overhead neglectable.
The following sections detail the execution of the classifier on the embedded platform. The [https://www.tensorflow.org/lite/performance/best_practices#tweak_the_number_of_threads number of threads] was also tweaked in order to test different configurations. During the execution, the well-know [https://en.wikipedia.org/wiki/Htop <code>htop</code>] utility was used to monitor the systemssystem. AlsoThis tool is very convenient to get some useful information such as cores allocation, processor load, the [https://www.tensorflow.org/lite/performance/best_practices#tweak_the_number_of_threads and number of running threads] was tweaked in order to test different configurations.
=== Floating-point model ===
==== Tweaking the number of threads ====
The following screenshots show the system status while executing the application varying with different values of the thread parameter.
</pre>
[[File:ML-TN-001 2 weightsquant default.png|thumb|center|600px|Thread parameter unspecified]]
==== Tweaking the number of threads ====
The following screenshots show the system status while executing the application varying with different values of the thread parameter.
[[File:ML-TN-001 2 fullquant default.png|thumb|center|600px|Thread parameter unspecified]]
== Results ==
The following table lists the prediction times for a single image to vary depending on the models model and threadsthe thread parameter.
{| class="wikitable" style="margin: auto;"
|+
|4
|80
|Interestingly, 7 actual processes are created besides the main one.
|}
