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Backprop is expensive. Not sure what the revolutionary aspect is here. Smells a bit like the Theranos of AI.

I am pretty sure this is about optimizing the graphs of trained models. That is, training is not freed from "the prison of supercomputers".

Exactly my point. Feed forward can run on much lower specs just fine. So their whole "prison of supercomputers" analogy is for the wrong side of the equation.

This is for optimizing inference. For consumer devices such as smart cameras, speech recognition on your phone, or robotics that's the part that matters.

They have real speed improvements by using 1-bit quantitzed weights and then running operations using 32-bit or 64-bit bitfields which happen to use very few instructions/cycles on modern CPUS: https://github.com/allenai/XNOR-Net

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