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

Info Box Applies to Machine Learning

Work in progress

Contents

HistoryEdit

Version	Date	Notes
1.0.0	September 2020	First public release

IntroductionEdit

Thanks to the unstoppable technology progress, nowadays Artificial Intelligence (AI) and specifically Machine Learning (ML) are spreading on low-power, resource constrained devices as well. In a typical Industrial IoT scenario, this means that edge devices can implement complex inference algorithms that were used to run on the cloud platforms only.

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

The idea is to develop one or more reference applications with the help of well-known frameworks/libraries and to test them on these platforms for comparing performances, resource utilization, development flow, etc.

In the following sections, these applications are described in more detail. Each article of this series explores in detail one specific platform or use case.

Reference application #1: fruit classifierEdit

This application implements a classifier like the one described here. There is one notable difference, however, with respect to the linked article. In this case, the model was created from scratch using TBD.

Model creationEdit

Articles in this seriesEdit

The other articles in this series are:

• Part 2: testing application #1 on Mito8M SoM (NXP i.MX8M)
• Part 3: testing application #1 on Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit
• Part 4: testing application #1 on NXP i.MX8M Plus EVK