Difference between revisions of "ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1"
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==Introduction== | ==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. | 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. | ||
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| + | 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. | ||
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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 well-known open-source frameworks/libraries and to deploy them on such platforms to compare performances, resource utilization, development flow, etc.
