Roughly seven years ago, Partha Ranganathan realized Moore’s law was dead. That was a pretty big problem for the Google engineering vice president: He had come to expect chip performance to double every 18 months without cost increases and had helped organize purchasing plans for the tens of billions of dollars Google spends on computing infrastructure each year around that idea.
But now Ranganathan was getting a chip twice as good every four years, and it looked like that gap was going to stretch out even further in the not-too-distant future.
So he and Google decided to do something about it. The company had already committed hundreds of millions of dollars to design its own custom chips for AI, called tensor processing units, or TPUs. Google has now launched more than four generations of the TPU, and the technology has given the company’s AI efforts a leg up over its rivals.
Google uses all kinds of custom hardware throughout its operations, but you rarely hear about it. This article provides some insight into the custom hardware Google uses for YouTube transcoding.