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ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1
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09:08, 26 July 2023
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{{InfoBoxTop}}
{{AppliesToMachineLearning}}
{{AppliesTo Machine Learning TN}}
{{InfoBoxBottom}}
[[File:TBD.png|thumb|center|200px|Work in progress]]
__FORCETOC__
|September 2020
|First public release
|-
|1.1.0
|November 2020
|Added new articles in the series
|}
The following block shows its architecture:
<
syntaxhighlight
pre
>
Model: "sequential"
_________________________________________________________________
Trainable params: 4,822,886
Non-trainable params: 0
</
syntaxhighlight
pre
>
The training was done in the cloud using an AWS EC2 server set up ad hoc.
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_3|Part 3: testing application #1 on Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit]]
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_4|Part 4: testing application #1 on NXP i.MX8M Plus EVK]]
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_5|Part 5: comparing NXP i.MX8M Plus NPU and Google Coral TPU]]
<!--
TBD
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-
_Part_5
_Part_6
|Part
5
6: testing application #1 on Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit with PyTorch
]]
-->
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