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{{AppliesTo Machine Learning TN}}
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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]].
This article compares in terms of performance a Machine Learning-based classification application when accelerated with different Neural Processing Units, namely the NXP i.MX8M Plus NPU and the [https://coral.ai/products/accelerator/ Google Coral Edge TPU].
The following box shows the output of the NPU-accelerated test.
<pre class="board-terminal">
root@imx8mpevk:/home/mathias/devel/test_coral# python3 image_classifier.py -m mobilenet_v1_1.0_224_quant.tflite -l labels.txt -i goldfish.jpg
INFO: Created TensorFlow Lite delegate for NNAPI.
Applied NNAPI delegate.
Warm-up time: 7832.38 ms
Original image size: (755, 355)
Cropped image size: (355, 355)
Resized image size: (224, 224)
Filling time: 0.73 ms
Inference time 1: 3.07 ms
Inference time 2: 2.98 ms
Inference time 3: 3.00 ms
Average inference time: 3.02 ms
Total prediction time: 3.75 ms
Results:
1.000 goldfish
0.000 toilet tissue
0.000 starfish
</pre>
==Test bedResults==As stated previously, it was not possible to use the fruit classifier application for testing. This is due to the fact Please note that the compiler for Coral TPU was not able to handle this model because of flatten layers. At the time of this article, in fact, this kind of layer [https://coral.ai/docssoftware/edgetpu/modelscan run either at 250 or 500 MHz].{| class="wikitable" style="margin: auto;"|+!Target!O.S.!Hardware accelerator!Total prediction time[ms]|-|NXP i.MX8M Plus EVK|Yocto Linux (release L5.4.24_2.1.0) |NPU|3.75|-|NXP i.MX8M Plus EVK|Yocto Linux (release L5.4.24_2.1.0)|TPU @ 250 MHz|8.03|-intro/#supported|NXP i.MX8M Plus EVK|Yocto Linux (release L5.4.24_2.1.0)|TPU @ 500 MHz|6.76|-operations was not listed among the supported types]|PC based on Intel(R) Pentium(R) Silver N5000 CPU @ 1.10GHz|Linux Parrot 4.10|TPU @ 500 MHz|2.94|}