I found the last paragraph the most interesting one: "[Parallelism] does not necessarily make sense elsewhere. Even in completely new areas that we don't do to

I found the last paragraph the most interesting one: "[Parallelism] does not necessarily make sense elsewhere. Even in completely new areas that we don't do today because you cant' afford it. If you want to do low-power ubiquotous computer vision etc, I can pretty much guarantee that you're not going to do it with code on a GP CPU. You're likely not even going to do it on a GPU because even that is too expensive (power wise), but with specialized hardware, probably based on some neural network model." Esp the last bit. I wonder what he means by "specialized hardware, probably based on some neural network model". These ones? http://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml http://www.research.ibm.com/cognitive-computing/neurosynapti... I'm doing Machine Learning / Deep Neural Network research, and of course, parallelism is very important for us. Our chair mostly trains on GPUs at the moment, but the big companies use heavily more parallelism and much bigger sizes, e.g. look at DistBelief from Google.