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Associate III
August 8, 2026
Solved

Can STM32N6 NPU be used directly from HAL without X-CUBE-AI?

Hi ST Community,

I am working with an STM32N657 and would like to use the integrated NPU directly from my own C/C++ application.

I understand that X-CUBE-AI together with the Neural-ART can generate the code required to execute a neural-network model on the STM32N6 NPU.

However, I would like to understand whether it is possible to use the NPU without X-CUBE-AI.

Does STM32CubeIde provide HAL APIs that allow an application to directly initialize, configure, load, and start the STM32N6 NPU?

AScha.3
AScha.3Best answer
Super User
August 8, 2026

Hi,

just from Gemini :

No, the STM32N657's Neural-ART Accelerator (NPU) cannot be used directly through standard STM32 HAL function calls without X-CUBE-AI or the underlying ST Edge AI Core runtime. [1, 2, 3, 4, 5]

Why You Need X-CUBE-AI / ST Edge AI Suite

  • Model Compilation: The Neural-ART Accelerator requires specialized binary instruction blocks ("epochs") and layer-to-hardware mapping generated by the ST Edge AI compiler. Standard HAL drivers do not parse or translate neural network models (like TensorFlow Lite or ONNX). [1, 3]
  • Memory Management: The NPU compiler dynamically assigns activation buffers and weights across specific internal NPU-RAMs and external memories based on bandwidth and performance constraints. This goes far beyond standard peripheral-oriented HAL memory handling. [1]
  • Runtime Execution: Interfacing with the NPU requires the Neural-ART runtime library to load, trigger, and retrieve inference outputs, which is packaged and instantiated through the X-CUBE-AI ecosystem rather than standalone HAL headers. [1, 2, 3]

If you're planning an implementation, tell me:

  • Are you trying to avoid code generation to build a custom low-level wrapper?
  • What AI framework format (e.g., .tflite, .onnx) is your model in?

I can share more details on how to interface a minimal runtime library with your application structure.

If you feel a post has answered your question, please click on " Best Answer ".

3 replies

AScha.3
AScha.3Best answer
Super User
August 8, 2026

Hi,

just from Gemini :

No, the STM32N657's Neural-ART Accelerator (NPU) cannot be used directly through standard STM32 HAL function calls without X-CUBE-AI or the underlying ST Edge AI Core runtime. [1, 2, 3, 4, 5]

Why You Need X-CUBE-AI / ST Edge AI Suite

  • Model Compilation: The Neural-ART Accelerator requires specialized binary instruction blocks ("epochs") and layer-to-hardware mapping generated by the ST Edge AI compiler. Standard HAL drivers do not parse or translate neural network models (like TensorFlow Lite or ONNX). [1, 3]
  • Memory Management: The NPU compiler dynamically assigns activation buffers and weights across specific internal NPU-RAMs and external memories based on bandwidth and performance constraints. This goes far beyond standard peripheral-oriented HAL memory handling. [1]
  • Runtime Execution: Interfacing with the NPU requires the Neural-ART runtime library to load, trigger, and retrieve inference outputs, which is packaged and instantiated through the X-CUBE-AI ecosystem rather than standalone HAL headers. [1, 2, 3]

If you're planning an implementation, tell me:

  • Are you trying to avoid code generation to build a custom low-level wrapper?
  • What AI framework format (e.g., .tflite, .onnx) is your model in?

I can share more details on how to interface a minimal runtime library with your application structure.

If you feel a post has answered your question, please click on " Best Answer ".
SaeidAuthor
Associate III
August 8, 2026

Hi ​@AScha.3 

Thanks for sharing your search results. 

I got the same result from ChatGPT, but I’d love to know if ST provides an NPU interface ( functions, library) to be used in an application code.

Have you implemented any AI application with STM32N6 ?

Julian E.
ST Technical Moderator
August 10, 2026

Hi ​@Saeid,

 

The information is correct, today you cannot use it directly.

One point, X Cube AI is not deprecated, please use STM32CubeAI Studio, a desktop application that should be easier to use.

STM32Cube AI Studio- The next-gen stand-alone tool for embedded AI - STMicroelectronics


This tool is able to support MX and MX2 targets.

 

For AI application on STM32N6, you can find example packages here:

STM32N6-AI | Software - STMicroelectronics

 

And in STM32Cube AI Studio, you can import a model and export a template application that only runs your model with random input data and print the output via serial.

 

Have a good day,

Julian

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