[darsnack]

Most neuromorphic chips are general purpose in that you can tune the parameters of the neuron model. You can run a variety of learning algorithms by tuning these parameters. The only thing that is fixed is the model of a neuron itself.

To have hardware that allowed any neuron model would boil down to having an array of MAC units, or even more generally, a DSP. At that point you’ve lost sight of your original goal with a neuromorphic chip — to build an energy-efficient, scalable neural emulator.

With respect to the academic research aspect — graduate labs can get their hands on neuromorphic hardware. GPUs weren’t made ubiquitous to process scientific algorithms. It was the researchers who had the ingenuity to use GPUs for general purpose compute. Access to a powerful GPU then was as straightforward as access to a neuromorphic chip is now. The issue is when you try to scale the problem you are solving. In this vein, a GPU cluster of that size is not currently available to academics. That’s why you see a bias towards deep learning research in industry. In other words, it doesn’t make sense to compare a server of neuromorphic chips (which is what is required to simulate realistic neural behavior) to a single GPU. Compare it to servers of GPUs like Google’s or FB’s. You’ll see that academics have poor resources in both cases and the research keeps moving on.