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According to [https://community.nxp.com/docs/DOC-343798 NXP documentation], ''eIQ Machine Learning Software is a collection of software and development tools for NXP microprocessors and microcontrollers to do inference of neural network AI models on embedded systems.''
This Technical Note (TN for short) illustrates hot how to use [https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ eIQ] in combination with Mito8M, one of the DAVE Embedded Systems's latest SoM's, which is built upon the [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 processor by NXP].
==Testbed==
For more information about the kernel and the root file system, please refer to the following section.
==Building NXP eIQ software==
NXP document [https://www.nxp.com/docs/en/nxp/user-guides/UM11226.pdf UM11226] illustrates how to build eIQ software support using Yocto Project tools. Even though the official procedure was tested against Ubuntu 16.04, a the build was completed successfully on host running Ubuntu 18.04 was used successfullyas well. By following step by step the official procedure,
The build process produces several artifacts
Regarding this TN, two of them are of interest
~/devel/eIQ/fsl-arm-yocto-bsp$ du -ch --max-depth=1
313M ./.repo
99M ./sources
30G ./downloads
147G ./build-xwayland
177G .
177G total
==Configuring the target==
==Running TensorFlow and TensorFlow Lite examples==
To verify that the root file system was generated properly, a couple of ready-to-use examples were run. Again, to execute them, please follow the procedure described in [https://www.nxp.com/docs/en/nxp/user-guides/UM11226.pdf UM11226].
The first one example makes use of TensorFlow:
<pre class="board-terminal">
root@imx8qmmek:~/devel/tensorflow# ls -la
