ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1

Info Box Applies to Machine Learning

History

Version	Date	Notes
1.0.0	September 2020	First public release

Introduction

This Technical Note (TN for short) is the first one of a series illustrating how machine learning-based inference applications perform across different embedded platforms, which are eligible for building intelligent edge devices.

The idea is to develop one or more applications with the help of well-known open-source frameworks/libraries and to deploy them on such platforms to compare performances, resource utilization, development flow, etc.

Test application #1: fruit classifier

This application implements a classifier like the one described MISC-TN-011:_Running_an_Azure-generated_TensorFlow_Lite_model_on_Mito8M_SoM_using_NXP_eIQ here. There is one notable difference, however. The model

TBD