Yeah, this is a line of research that's been ongoing for a while and "CPUs not GPUs" seems like of it's rallying cry but this seems involve multiple incomparables - like a combination of apple-to-orange and banana-to-peaches comparisons (different algorithms on different platforms doing different tasks judged according to a different success criteria, jeesh).

The thing is there's no benchmark for just neural-netting. Neural nets do well on a variety of image recognition tasks but the nets that do well as particular trained instances on particular data. And, moreover, it's not just doing well on a benchmark but generalizing well. Essentially, neural nets simply algorithms but paradigms (for good and ill).

Instead of the babble about CPU vs GPU, they should be talking the strengths and weaknesses of their approach to image recognition and related tasks. Their locality sensitive hash approach is certainly interesting and some parts of their approach might hypothetically be useful for dealing with the weaknesses of the neural net approach (fragility, black-box-unexplainability, etc).