To be fair, the goal of those new computing architectures is not to advance algorithms. That at best is a side benefit. Those algorithmic performance improveme

To be fair, the goal of those new computing architectures is not to advance algorithms. That at best is a side benefit. Those algorithmic performance improvements seen in DNN have come from improved datasets and training. Numenta and IBM arent focusing on training AFAIK. Google's Quantum Annealing [0] is the only hardware I'm aware of focused on training, although there are rumors Nervana Systems may produce something [1]. I'm sure there are others; accelerating training of DNN isnt a particularly new idea. The goal of these other computing architectures is typically to provide lower power, higher frequency/lower latency, or smaller form factor execution of trained models, but there is a question of how much value they can provide over more conventional chips to be worth the chip design costs. However without these architectures becoming as mainstream as say a GPU, I think we will continue to see advances come from the typical everyday computer. The ML community seems to be much more democratic than others. [0] http://googleresearch.blogspot.com/2015/12/when-can-quantum-annealing-win.html http://googleresearch.blogspot.com/2015/12/when-can-quantum-... [1] http://www.nervanasys.com/about/ http://www.nervanasys.com/about/