Difference between revisions of "ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1"
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=== Model creation === | === Model creation === | ||
| + | |||
| + | ==Articles in this series== | ||
| + | The other articles in this series are: | ||
| + | *[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_2|Part 2]] | ||
| + | *[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_3|Part 3]] | ||
| + | *[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_4|Part 4]] | ||
| + | <!-- | ||
| + | *[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_5|Part 5]] | ||
| + | --> | ||
Revision as of 14:52, 7 September 2020
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Contents
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 here. There is one notable difference, however. In this case, the model was created from scratch using TBD.
Model creation[edit | edit source]
Articles in this series[edit | edit source]
The other articles in this series are:
