How will future microprocessors handle applications like machine learning?

 Although it’s not as well known as Moore’s Law, an interrelated concept is Dennard scaling: This states that as transistors get smaller, their power density stays constant so that the power use stays in proportion with area. For a long time, this meant that increases in density provided by process shrinks didn’t lead to a proportional increase in power requirements and cooling needs.

Dennard scaling began to break down in 2006 or so. Today, to the degree that we can continue to fabricate incrementally smaller transistors, powering and cooling them is an increasingly critical need. As with reconfigurability, very fine-grained and dynamic control of power is increasingly a requirement of microprocessors. It doesn’t do any good to have more transistors if you can’t turn them on.CMOS process shrinks have been a singularly important foundation for great swaths of modern technology. The slowing and, presumably, the eventual ending of that particular technology tree will have effects. However, as we saw at the Hot Chips conference, there are other paths forward to progress, even if they may not be as regular and at least somewhat predictable as shrinking transistors have been.

They may require rethinking some of our computer engineering careers — especially given that important application classes, such as machine learning, have such different characteristics than many of our more traditional applications.