I think this depends a lot on the use case. I envision for the most part this would be used in/on large known clusters of computation, as an independent check on computation usage and a failsafe. In that case it will be pretty easy to distinguish from other uses like gaming or cryptocurrency mining. If we’re in the regime where we’re worried about sneaky efforts to assemble lots of GPUs under the radar and do ML with them, then I’d expect there would be pattern analysis methods that could be used as you suggest, or the system could be set up to feed back more information than just computation usage.
I think this depends a lot on the use case. I envision for the most part this would be used in/on large known clusters of computation, as an independent check on computation usage and a failsafe. In that case it will be pretty easy to distinguish from other uses like gaming or cryptocurrency mining. If we’re in the regime where we’re worried about sneaky efforts to assemble lots of GPUs under the radar and do ML with them, then I’d expect there would be pattern analysis methods that could be used as you suggest, or the system could be set up to feed back more information than just computation usage.