IA Generador neural network library for microcontroller

Interesting and I can see this helping others, especially in the teaching world. It would help if you provide a bit of history and what's the background to this project. Based on the contact email, you are involved in education/teaching and robotics.

Otherwise, a key issue will be to explain what happens to your data. Is it processed locally or is it on the cloud and then what. Where does your data go.

rbasulto53 said:

thanks

This is an interesting project, especially with machine learning moving toward smaller devices and microcontrollers. Lightweight neural network libraries seem really useful for processing data locally without depending too much on powerful hardware or cloud services. I’d be interested to see how these models handle limited memory and processing power in real-time applications. I’ve also been exploring practical tech and gaming projects at astutebetaserver

hi,
We ran tests on the rp2040 and obtained these measurements (execution time of already trained model)
- 3 inputs, 2 hidden layers, and 6 neurons per layer with ReLU activation function: 3.5 ms
- 3 inputs, 4 hidden layers, and 10 neurons per layer with ReLU and Leaky activation function: 7.1 ms

Ready version 2.0.
https://traindeep.ai

thanks for you comment.  Please check out the new version 2.0. It has much of what you mentioned and much more.
https://traindeep.ai

If you want anyone to bother with your link, you should greatly expand your description of what is at the end of the link.

It generates a trained NN. So what?

Give me a useful example so I can be intrigued about the possibilities.

Provide a walk through of the process so I can see how easy it is to use.

What is the size of the resulting output? How useful is it? How limited is its application?

You have to make a reasonable effort to sell it if you want anyone to kick the tires. 

Thank you very much for your comment. Yes, I will be running performance tests, although the generated library is very lightweight for high performance and low resource consumption.
Another important difference is that it doesn't require installing anything to create the model, as it's trained on the web, and you don't need any libraries to run the trained model; it's very simple.

This is a really interesting project! I've been exploring the challenges of running neural networks on microcontrollers myself. Have you had a chance to compare the performance of IA Generador against other lightweight libraries like emlearn or NNoM? I'd be particularly interested in seeing a benchmark for inference speed on a Raspberry Pi Pico or an STM32 board. I'm excited to see where this project goes. Keep up the great work!

Thanks for the welcome, this application is to help the community and has no commercial purpose.

What would be my motivation, to have me click on a link, from a user with a profile of 25 points.

That score would suggest you are new to the E14 community. Welcome.