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→Introduction
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].
Originally, the idea was to use the classifier described in [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1#Reference_application_.231:_fruit_classifier|this section]], which was already tested with the i.MX8M Plus NPU as described [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_4|in this TN]]. This would have allowed to compare the results with the other tests run with the '''same''' classifier documented in [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|this series]] too. However, this idea had to be discarded because of unexpected difficulties detailed in the following sections.
==Test bed==
As stated previously, 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].
