I am currently researching ways to export models that I trained with Pytorch on a GPU to a microcontroller for inference. Think CM0 or a simple RISC-V. The ideal workflow would be to export c-sourcecode with as little dependencies as possible, so that it is completely platform agnostic.

What I noticed in general is that most edge inference frameworks are based on tensorflow lite. Alternatively there are some closed workflows, like Edge Impulse, but I would prefer locally hosted OSS. Also, there seem to be many abandoned projects. What I found so far:

Tensorflow lite based

• Tensorflow lite
• TinyEngine from MCUNet. Looks great, targeting ARM CM4.
• CMSIS-NN. ARM centric. Examples. They also have an example for a pytorch to tflite converter via onnx
• TinyMaix. Very minimalistic, can also be used on RISC-V
• nnom

Pytorch based

• PyTorch Edge / Executorch Sounds like this could be a response to tflite, but it seems to target intermediate systems. Runtime is 50kb…
• microTVM. Targeting CM4, but claims to be platform agnostic.

ONNX

• DeepC. Open source version of DeepSea. Very little activity, looks abandoned
• onnx2c - onnx to c sourcecode converter. Looks interesting, but also not very active.
• cONNXr - framework with C99 inference engine. Also interesting and not very active.

Are there any recommendations out of those for my use case? Or anything I have missed? It feels like there no obvious choice for what I am trying to do.

Most solutions that seem to hit the mark look rather abandoned. Is that because I should try a different approach or is the field of ultra-tiny-ml OSS in general not so active?