Free tool for Edge AI developers

STM32Cube.AI allows you to optimize and deploy trained Neural Network models from the most popular AI frameworks on any STM32 microcontroller.
The tool is available via a graphical interface in the STM32CubeMX environment as well as in command line.

New in version 7.3


Addition of 3 optimization options
(RAM, time or balanced)
Performance enhancement
Support for TF v2.10 models

From Neural Networks to STM32 optimized code

Identify the right STM32 MCU for your project and generate the suitable code from your trained Neural Network model

1
Load NN model
2
Analyse NN model
3
Validate
4
Optimize
5
Generate code
Select your MCU and load your trained model from your favorite AI framework: Tensorflow, Pytorch, ONNX, Scikit-Learn.. STM32Cube.AI supports FLOAT32 or quantized INT8 weight formats (input file formats: .tflite, .h5, and .onnx).
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The model analysis provides access to a complete set of information about your model, such as the number of parameters, MACC/layer complexity, and detailed RAM and flash size requirements.
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Validate your model with a data set or random values to check that the generated C-code matches with the original trained model supplied. Validation options can be performed on the desktop computer or on the STM32 board connected to the computer.
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Optimize your model by managing memory usage by layer and choosing the right balance between internal and external memory resources.
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Generate the optimized C-code of your AI inference model.
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Make the most of your STM32 microcontroller

By optimizing the memory use and the inference time of your AI models, STM32Cube.AI ensures they can easily run on microcontrollers.
STM32Cube.AI is the most efficient free NN code generator for MCU!

Up to

20 %

space freed-up in FLASH and RAM*

Up to

60 %

faster inference time*

* versus TensorFlow Lite for microcontroller

Get started with STM32Cube.AI

Discover how to optimize your AI Neural Network and create processing libraries for your STM32 project