But I don't think that's the goal here. I view this type of research as part of early efforts in a long-term quest to discover if it's possible to build "neuromorphic" devices capable of universal computation that are much more efficient than current methods. These efforts today seem to be roughly at the same stage that "deep learning" was in the late 1970's to early 1980's, or even at an earlier stage.
For what it's worth, I also think it's worthwhile to develop hardware/software that accelerates linear/multilinear algebra operations and/or makes it practical for us to use more general/flexible abstractions in our code.[a]
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[a] For example, I would love to be able to model each artificial neuron as a separate object in code, making it trivial to group/ungroup neurons into irregularly shaped and/or overlapping groups and easily shrink/expand/alter such groups on the fly without incurring a performance penalty. Right now, that's not possible: If we want decent performance, we have little choice but to pigeonhole all our designs into functions that operate on grid-like shapes (equal-sized vectors, matrices, higher-order tensors) and resort to thinks like masking so we can have fast multiply-sum operations.