STM32 MCUs Machine learning & AI

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Edge AI for all. NanoEdge AI Studio autoML tool, now free for STM32 and open to all ARM Cortex-M bas

NanoEdge AI Studio v4.3 is now offered for free to all engineers, giving them the opportunity to create highly efficient TinyML libraries that can be deployed on Cortex-M based microcontrollers. In addition, other enhancements have been integrated to...

ST30449_nanoedge-ai-studio-motor (2).jpg ST30449_nanoedge-ai-studio-motor (2).jpg

machine learning inputs!!?!

  I wanted to do a project with cube.ai, but as I found out, most projects done with machine learning have multiple input features.As for my project, I have only one feature which is the most prominent featureNow my question is, can I use machine lea...

hkane.1 by Associate II
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Resolved! How does the aiValidation in X-CUBE-AI run?

We are running X-CUBE-AI on an stm32F746 eval board (and a number of other stm32 eval boards). We are using version 7.1.0, 7.2.0, and 7.3.0, and using the generated source code for CubeIDE.We then pulse a GPIO whenever ai_mnetwork_run() runs, which w...

MChan.10 by Associate II
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Why the flash and used ram used do not change when I use the dynamic range quantization for tensorflow lite model? (analyzing the neural network in STM32 X-Cube-AI)

Hi! I am doing some experiements to analyze the neural network model. The model I am using is the HAR model example. You can see here: https://github.com/ausilianapoli/HAR-CNN-Keras-STM32So I am doing some experiments regarding the quantization.First...

HLU.1 by Associate
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