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I haven't read the paper, but most of the time, in order to achieve matching outputs with less layers, you will need exponentially more neurons per layer.

If you have infinite parallelism I believe the shallow network would be faster, but deep networks will use less total operations and will be faster in practice.

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+1

This is how it is for logic design using gates, and I believe that's not a coincidence.

Finding the right optimum between parallel and deep for logic design is computationally intensive itself, hence it often comes down to experiencial learning. The latter implies potential use of machine learning for that optimization. And it contunues ... lived happily thereafter. :-)

There may be some utility to optimizing a given neural network to exploit the features of a given computational architecture. The point would be to expand the width of each layer to hit the limits of your hardware's parallelism. I.e., partition the network into n parts and shallow-ize each one to produce a new neural network with n layers, each of which being roughly as wide as your p parallel computational channels.

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