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==Test bed==
As stated previously, unfortunately it was not possible to use the fruit classifier application for testing. This is due to the fact 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/docs/edgetpu/models-intro/#supported-operations was not listed among the supported types]. They could have been modified in order to make it compatible with the Coral compiler, but this would have made impossible a direct comparison with previous results anyway. Thus, we decided to use a completely different model (namely, mobilenet) and limit the comparison between the use of the NXP NPU and the Google Coral TPU. The tests were run on an NXP i.MX8M Plus EVK connected to a [https://coral.ai/products/accelerator Coral USB Accelerator] via USB3 port. For the sake of completeness, the same test application was run on a PC connected to the USB accelerator as well. This test is useful to verify if and how much the performance of the TPU is affected when working in tandem with the i.MX8M Plus.
The following box shows the output of the NPU-accelerated test.
==Results==
Please note that the TPU [https://coral.ai/software/ can run either at 250 or 500 MHz].
{| class="wikitable" style="margin: auto;"
|+
!Target
[ms]
|-
|NXP i.MX8M PlusEVK
|Yocto Linux (release L5.4.24_2.1.0)
|NPU
|3.75
|-
|NXP i.MX8M PlusEVK
|Yocto Linux (release L5.4.24_2.1.0)
|TPU @ 250 MHz
|8.03
|-
|NXP i.MX8M PlusEVK
|Yocto Linux (release L5.4.24_2.1.0)
|TPU @ 500 MHz
|6.76
|-
|PC based on Intel(R) Pentium(R) Silver N5000 CPU @ 1.10GHz|Linux Parrot 4.10|TPU @ 500 MHz
|2.94
|}