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I ended up hand rolling a custom micropython module for the S3 to do a proof of concept handwriting detection demo on an ESP32, might be interesting to some.

https://luvsheth.com/p/running-a-pytorch-machine-learning

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Great post with very interesting detail, thanks ! Another optimization could be to quantize the model, this transform all compute as int compute and not as floating point compute. You can lose some accuracy, but for any bigger model it's a requirement ! Espressif do a great job on the TinyML part, they have different library for different level of abstraction. You can check https://github.com/espressif/esp-nn that implement all low level layers. It's really optimized and if you use the esp32-s3 it will unlock a lot of performance by using the vector instructions.

You are right I should definitely be looking into how to run these models as ints as well, especially with the C optimizations to micropython you would see a lot larger performance gains using ints compared to floats. Definitely need to find some time to try it!

On the other hand the tinyML library looks great too and if I was going to do this for a product that would likely be the direction I would end up taking just cause it would be more extensible and better supported.

Thank you for the links!

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