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The Raspberry Pi provides documentation for their GPU architecture, so it would be possible to provide support for that within open source machine learning frameworks. It would involve quite a bit of work, though, and the RPi is not really competitive with modern hardware in performance-per-watt terms, even when using GPU compute.





I believe, Idein did that. At least they regularly post impressively (for the Pi) fast examples to /r/raspberry_pi like https://redd.it/a5o6ou. It seems the result isn't available individually or as open source but only in the form of a service (https://actcast.io/)

There are some well optimised libraries, for example a port of darknet that uses nnpack and some other Neon goodies. You can do about 1fps with tiny yolo. Not sure if it used anything on the gpu though.

NEON is the CPU SIMD feature, it has nothing to do with the vc4 GPU.

Yes, I know. My point was that CPU-only deep learning is possible on the Pi if you don't need real-time inference. What I wasn't sure of is whether that specific port does anything on the GPU at all, or if it's only using NEON intrinsics.



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