dave_user, Administrators
5,190
edits
Changes
no edit summary
{{InfoBoxTop}}
{{AppliesToMachineLearning}}
{{AppliesTo Machine Learning TN}}
{{AppliesToMito8M}}
{{AppliesTo MITO 8M TN}}
{{InfoBoxBottom}}
__FORCETOC__
|-
|1.0.0
|September October 2020
|First public release
|}
==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].
=== Environment Test bed ===
The kernel and the root file system of the tested platform were built with the L4.14.98_2.0.0 release of the Yocto Board Support Package for i.MX 8 family of devices. They were built with support for [https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ eIQ]: "a collection of software and development tools for NXP microprocessors and microcontrollers to do inference of neural network models on embedded systems".
The following table details the relevant specs of the test bed. {| class="wikitable" style="margin: auto;"|-|'''NXP Linux BSP release'''|L4.14.98_2.0.0|-|'''Inference engine'''|TensorFlow Lite 1.12|-|'''Maximum ARM cores frequency''' '''[MHz]'''|1300|-|'''SDRAM memory frequency (LPDDR4)''''''[MHz]'''|1600|-|'''[https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt Governor]'''|ondemand|} ==Model deploymentand inference application==To run the model on the target, a new C++ application was written. After debugging this application on a host PC, it was migrated to the edge device where it was built natively built. The root file system for eIQ, in fact, provides the native C++ compiler as well.
The application uses OpenCV 4.0.1 to pre-process the input image and TensorFlow Lite (TFL) 1.12 as inference engine. The model, originally created and trained with Keras of TensorFlow (TF) 1.15, was therefore converted into the TFL format.
Then, the same model was recreated and retrained with Keras of TF 1.12. This allowed to convert it into TFL with post-training quantization of the weights without compatibility issues with the target inference engineversion.
After that, it was also recreated and retrained with quantization-aware training of TF 1.15. In this way, a fully quantized model was obtained after conversion.
So, it in the end, three converted models were obtained: a regular 32 -bit floating -point one, an 8 -bit half -quantized (only the weights, not the activations) one, and a fully -quantized one.
The following images show the graphs of the models before conversion (click to enlarge):
{| class="wikitable" style="margin: auto;"
|+
|-
|[[File:ML - Keras1.15 fruitsmodel.png|none|thumb|1000x1000px]]
{| class="wikitable" style="margin: auto;"
|+
|-
|[[File:ML - TFL float fruitsmodel.png|none|thumb|1000x1000px]]
* The inference was repeated several times and the average execution time was computed
* 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 system. This tool is very convenient to get some useful information such as cores allocation, processor load, and number of running threads.
=== Floating-point model ===
<pre class="board-terminal">
root@imx8qmmek:~/devel/image_classifier_eIQ# ./image_classifier_cv 2 my_converted_model.tflite labels.txt testdata/red-apple1.jpg
</pre>
[[File:ML-TN-001 2 float default.png|thumb|center|600px|Thread parameter unspecified]]
[[File:ML-TN-001 2 float 1thread.png|thumb|center|600px|Thread parameter set to 1]]
[[File:ML-TN-001 2 float 2threads.png|thumb|center|600px|Thread parameter set to 2]]
=== Half-quantized model ===
<pre class="board-terminal">
root@imx8qmmek:~/devel/image_classifier_eIQ# ./image_classifier_cv 2 my_fruits_model_1.12_quant.tflite labels.txt testdata/red-apple1.jpg
</pre>
The following screenshot shows the system status while executing the application. In this case, the thread parameter was unspecified.
[[File:ML-TN-001 2 weightsquant default.png|thumb|center|600px|Thread parameter unspecified]]
=== Fully-quantized model ===
<pre class="board-terminal">
root@imx8qmmek:~/devel/image_classifier_eIQ# ./image_classifier_cv 3 my_fruits_model_qatlegacy.tflite labels.txt testdata/red-apple1.jpg
1 Red Apple
</pre>
==== Tweaking the number of threads ====
The following screenshots show the system status while executing the application with different values of the thread parameter.
[[File:ML-TN-001 2 fullquant default.png|thumb|center|600px|Thread parameter unspecified]]
[[File:ML-TN-001 2 fullquant 4threads.png|thumb|center|600px|Thread parameter set to 4]]
== Results ==