They have been designed, and are being used either for more efficient pattern matching, or to speed up brain simulations (again, using known neuronal models).
You seem to expect something else from neuromorphic computing, why?
> [the truenorth team had] to shoehorn a convnet on a chip that really wasn't designed for it. I mean, if the goal was to run a convnet at low power, they should have built a chip for that. The performance (and the accuracy) would be a lot better than this.
They used their 'neuromorphic' chip in an explicitly non-neuromorphic way, basically approximately mapping deep learning processes to their chip. There is very little neuromorphicity (brain-likeness) about it (plasticity rules out of their ass, for starters). And they still get less than state-of-the art performance in most tasks!
I expect 'neuromorphic' to be used when sound neuroscience is used in large scale implementations that allow us to actually simulate parts of the brain. Anything else, we call it what it is, ANNs.
Well, none of those chips are brain-like at all. For example, TrueNorth is fully digital, it uses separate compute/memory blocks, signal multiplexing, signal encoding, routing protocols, instruction set, etc, none of which is in any way related to what brain is doing. What makes you think it's "neuromorphic"?
Whether you like it or not, get used to people calling their hardware ANN implementations "neuromorphic".
Nope, neuromorphic means the hardware would simulate the neurobiology, not ANNs. More practically, they would never publish in Science if their title was "printing ANNs in hardware".
TrueNorth hardware, as I illustrated, does not resemble neurobiology at all. There are no brain-like components there, on any level. Moreover, it can run ANN algorithms just as easily as more "neuromorphic" algorithms.
Pointing to how they chose to name it for publication is not exactly a very convincing argument to support your view, is it? :)
My point is architectures like TrueNorth are very impressive from the point of view of a computer engineer, and they are very efficient when running their intended applications (neural network algorithms). The fact that they are not "brain-like" does not make them any less impressive.
They have been designed, and are being used either for more efficient pattern matching, or to speed up brain simulations (again, using known neuronal models).
You seem to expect something else from neuromorphic computing, why?