The problem is that we have designed ourselves into an architectural cul-de-sac when it comes to processors. We have fifty-plus years of evolution on programming methodologies built on top of von Neumann architectures. Moore's Law has given us decades of exponential gain without significant challenge to that architecture, and now that Moore's Law is reaping diminishing returns in terms of compute performance we are in the situation where we'd have to go backward forty years on our programming model in order to take advantage of a superior (given today's technology) architecture. For example, FPGAs can in many cases outperform von Neumann machines by orders of magnitude in terms of compute performance and (more importantly) performance per watt. However, the programming model and ecosystem for FPGAs is worse than primitive. Something you could write in a couple hundred lines of C code could take months to get up and running on an FPGA. We need a way to transition from von Neumann computing to alternative architectures without starting over on computer science. Or, perhaps recent trends in neural networks will eliminate the need for that?