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It's becoming popular to want to push machine learning tasks onto edge devices like mobile and desktop CPUs, for example apps that include some machine learning
by greglindahl 8y ago
It's becoming popular to want to push machine learning tasks onto edge devices like mobile and desktop CPUs, for example apps that include some machine learning. Some of these machine learning algorithms do a lot of matrix multiplies.
"Typical" is highly varied, and it changes.
Edit: here's an example: Google brings on-device machine learning to mobile with TensorFlow Lite
https://thenextweb.com/artificial-intelligence/2017/11/15/google-brings-on-device-machine-learning-to-mobile-with-tensorflow-lite/ https://thenextweb.com/artificial-intelligence/2017/11/15/go...
- kbenson 8y agoWould they be using mostly CPU for that, or would they offload it to the GPU or a dedicated chip? I would assume you would use your general purpose CPU only if all else wasn't available (and generally there's a GPU available on most end user devices these days).
- greglindahl 8y agoIf possible the GPU, but not all GPUs have either a library or enough documentation to write one. I’ve seen complaints about this issue on mobile GPUs for years, no idea how widespread it is now. BTW, this is just one example algorithm that I picked because it does (on the cpu) what the person I replied to said was rare.
- rbanffy 8y agoRunning the model is much easier than training it. On power-constrained environments, DSPs can do it.