During modern computing’s first epoch, one trend reigned supreme: Moore’s Law.
Actually a prediction by Intel Corp. co-founder Gordon Moore rather than any sort of physical law, Moore’s Law held that the number of transistors on a chip doubles roughly every two years. It also meant that performance of those chips—and the computers they powered—increased by a substantial amount on roughly the same timetable. This formed the industry’s core, the glowing crucible from which sprang trillion-dollar technologies that upended almost every aspect of our day-to-day existence.
As chip makers have reached the limits of atomic-scale circuitry and the physics of electrons, Moore’s law has slowed, and some say it’s over. But a different law, potentially no less consequential for computing’s next half century, has arisen.
I call it Huang’s Law, after Nvidia Corp. chief executive and co-founder Jensen Huang. It describes how the silicon chips that power artificial intelligence more than double in performance every two years. While the increase can be attributed to both hardware and software, its steady progress makes it a unique enabler of everything from autonomous cars, trucks and ships to the face, voice and object recognition in our personal gadgets.
Between November 2012 and this May, performance of Nvidia’s chips increased 317 times for an important class of AI calculations, says Bill Dally, chief scientist and senior vice president of research at Nvidia. On average, in other words, the performance of these chips more than doubled every year, a rate of progress that makes Moore’s Law pale in comparison.
