ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 5
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | November 2020 | First public release |
Introduction[edit | edit source]
This Technical Note (TN for short) belongs to the series introduced 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 Google Coral Edge TPU.
Originally, the idea was to use the classifier described in this section, which was already tested with the i.MX8M Plus NPU as described in this TN. This would have allowed to compare the results with the other tests run with the same classifier documented in this series too. However, this idea had to be discarded because of unexpected difficulties detailed in the following sections.
Test bed[edit | edit source]
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 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 and limit the comparison between the use of the NXP NPU and the Google Coral TPU. The tests were run on a NXP i.MX8M Plus EVK connected to a 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.
