Executorch: On-device AI across mobile, embedded and edge for PyTorch
3 comments
·December 18, 2025Scene_Cast2
lukeinator42
yeah that checks out, although looks like they do have an example for running models on a raspberry pi pico 2: https://docs.pytorch.org/executorch/main/pico2_tutorial.html. The list of embedded platforms this can run on is probably greater than the list of backends, it just wouldn't have acceleration.
nickpsecurity
Yeah, it's targeting "micro"-controllers, not microcontrollers. I was hoping for a PyTorch solution to TF Lite.
This is still great, though. Previously, I thought a mobile model (eg speech/object recognition) would require me to learn both PyTorch and something like MLC in C++. Then, port them.
If this is as it appears, I could develop a small model that could run on mobile on my laptop, train it on cloud GPU's, test it locally, and use this tool to produce a mobile version (or save some steps?). That would keep us from having to learn C++ or MLC just to do mobile.
I mean, one still can learn other tools for their advantages. However, ML students and startups might benefit greatly from this by being able to rapidly develop or port mobile apps. Then, people learning other tools for their advantages build stuff that way. The overall ecosystem gets stronger with more competition.
I've heard from a friend who works in the embedded space that Tensorflow Lite is still the only realistic (supported by vendors) game in town for running ML models on microcontrollers such as ESP32, nRF, etc. The hardware support listed for this project seems like it's targeting much "fatter" MCUs (Android, etc).