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{{AppliesToMachineLearning}}
{{AppliesTo Machine Learning TN}}
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{{AppliesTo MITO 8M TN}}
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==Introduction==
This Technical Note (TN for short) belongs to the series introduced [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|here]].
Specifically, it illustrates the execution of an inference application (fruit classifier) that makes use of the model described in [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1#Reference_application_.231:_fruit_classifier|this sectioninference application (fruit classifier)]] when executed on the [[:Category:Mito8M|Mito8M SoM]], a system-on-module based on the NXP [https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors/i-mx-8m-family-armcortex-a53-cortex-m4-audio-voice-video:i.MX8M i.MX8M SoC].
=== Test bed ===
The following table details the relevant specs of the test bed.
{| class="wikitable" style="margin: auto;"
|-
|'''NXP Linux BSP release'''
|unspecified
|200
|Four threads are created beside the main process (supposedly, this quantity is set accordingly to the number of physical cores available). Nevertheless, they seem to be constantly in sleep state.
|-
|4
|8084|Interestingly, 7 actual processes are created besides beside the main one. Four of them, however, seem to be constantly in sleep state.
|}
The same tests were repeated using a network file system (NFS) over an Ethernet connection, too. No significant variations in the prediction times were observed.
In conclusion, to maximize the performance in terms of execution time, the model has to be fully-quantized and the number of threads has to be specified explicitly.