Skip to main content
Senior
August 18, 2022
Solved

About ML and AI Development Environment. What is the difference between X-CUBE-AI and NanoEdge AI Studio.

  • August 18, 2022
  • 2 replies
  • 2346 views

Hi Team

I want to know the difference between X-CUBE-AI and NanoEdge AI Studio for ML and AI development environment.

  • Can I do the same thing with either dev environment?
  • Are there any restrictions on the devices used?
This topic has been closed for replies.
Best answer by Julian E.

Hi Kenji,

To complete Peter BENSCH answer, both tools are complementary:

X-CUBE-AI is used to convert pre trained neural networks into an optimized code for STM32 microcontroller. Which means that, beforehand, you must create a neural network that solves your problem, yourself (with Pytorch or TensorFlow for example)

NanoEdge AI Studio in the other hand, generate ML solution from scratch by benchmarking various model and selecting the best Machine Learning library given your data (it is an autoML software). NanoEdge will then generate C libraries that you can implement on your embedded code for your STM32 MCU.

NanoEdge can achieve good results with small models, small datasets and has low RAM/FLASH usage. X-CUBE-AI can be used for bigger models such as CNN for computer Vision. So given your use case and your state of progress you might choose to use one solution or the other.

You can find much more information here:

Artificial intelligence ecosystem for STM32 - STMicroelectronics

Here is the link to the documentation of all AI solutions: https://wiki.st.com/stm32mcu/wiki/Category:Artificial_Intelligence

2 replies

Peter BENSCH
Technical Moderator
August 18, 2022

X-CUBE-AI was developed by STMicroelectronics and is a free-to-use machine learning tool with good capabilities.

NanoEdge AI Studio was developed by Cartesiam, which was acquired by STMicroelectronics in 2021, and can unleash the full power of machine learning. It can be tested free of charge for 3 months and then requires an annual licence (see DB4564).

Both use very different approaches, which is why two separate tools are currently necessary.

Does it answer your question?

Regards

/Peter

Kenji1Author
Senior
August 18, 2022

Hi @Peter BENSCH​ 

Yes, thank you!

Julian E.
Julian E.Best answer
Technical Moderator
October 12, 2022

Hi Kenji,

To complete Peter BENSCH answer, both tools are complementary:

X-CUBE-AI is used to convert pre trained neural networks into an optimized code for STM32 microcontroller. Which means that, beforehand, you must create a neural network that solves your problem, yourself (with Pytorch or TensorFlow for example)

NanoEdge AI Studio in the other hand, generate ML solution from scratch by benchmarking various model and selecting the best Machine Learning library given your data (it is an autoML software). NanoEdge will then generate C libraries that you can implement on your embedded code for your STM32 MCU.

NanoEdge can achieve good results with small models, small datasets and has low RAM/FLASH usage. X-CUBE-AI can be used for bigger models such as CNN for computer Vision. So given your use case and your state of progress you might choose to use one solution or the other.

You can find much more information here:

Artificial intelligence ecosystem for STM32 - STMicroelectronics

Here is the link to the documentation of all AI solutions: https://wiki.st.com/stm32mcu/wiki/Category:Artificial_Intelligence

​In order to give better visibility on the answered topics, please click on 'Accept as Solution' on the reply which solved your issue or answered your question.
Kenji1Author
Senior
October 17, 2022

Hi @Julian E.​ -san

Thank you for the information.