[dkfellows]

> Specifically, how will you prevent SpiNNaker from going down the same path as the Connection Machine - (stops doing AI stuff because, say, geneticists want to use it for protein sequencing)?

I'm on the team. I can say that we're specifically funded to do and support computational neuroscience. However, if someone comes along with money wanting to do other kinds of projects on the hardware (and are able to handle the special characteristics of the machine itself) then they're welcome. The challenge is that it's non-conventional in a number of ways that make porting code tricky: in particular, the messages are small, designed to be multicast rather than unicast, the instruction memory per core is only 32kB, and there's no hardware floating point at all in the current generation. (You can do floating point in emulation. We do that in one of our projects.)

> Why do you see this as the future over something like NVIDIA's new HGX-2 or clusters of TPUs?

We see it as solving different problems. Those approaches you mention are great for solving problems that resolve to big matrix operations; SpiNNaker is better at tackling problems that are dominated in terms of description by communication. Neural simulations are really just vast hybrid ODE systems, but where it is possible to break up the simulation into lots of communicating domains (the synapses and neurons).

[peterlk]

I'm a bit late in checking up on this, but just wanted to say thanks for taking the time to answer some questions