Difference between revisions of "ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1"
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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. | 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 well-known open-source frameworks/libraries and to deploy them on such platforms to compare performances, resource utilization, development flow, etc. | + | 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== | ==Test application #1: fruit classifier== | ||
Revision as of 13:56, 4 September 2020
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History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | September 2020 | First public release |
Introduction[edit | edit source]
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[edit | edit source]
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
