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.
But as Google was developing the TPUs, the company figured out that AI wasn’t the only type of computing it could improve. When Ranganathan and the other engineers took a step back and looked at the most compute-intensive applications in its data centers, it became clear pretty quickly what they should tackle next: video.
“I was coming at it from the point of view of, ‘What is the next big killer application we want to look at?’” Ranganathan said. “And then we looked at the fleet, and we saw that Transcoding was consuming a large fraction of our compute cycle.”
YouTube was by far the largest consumer of video-related computing at Google, but the type of chips it was using to ingest, convert and play back the billions of videos on its platform weren’t especially good at it. The conversion part is especially tricky, and requires powerful chips in order to do it efficiently.
And so converting, or transcoding, videos into the correct format for the thousands of devices that would end up playing them struck Ranganathan as a good problem to spend some time on. Transcoding is very compute-intensive, but at the same time, the task itself is simple enough that it would be possible to design what’s called an application-specific integrated circuit, or ASIC, to get the job done.
“For something like transcoding, which is a very specific, high-intensity sort of workload, they can get an awful lot of bang for their buck there,” chip industry analyst Mike Feibus said.
To get management to greenlight the project in 2016, Ranganathan’s colleague Danner Stodolsky sent an instant message to YouTube vice president Scott Silver, who oversaw the company’s sprawling infrastructure. He asked for about 40 staff members and an undisclosed dollar amount in the millions to make it happen, Silver said.
“It was very, very quick because it just made sense looking at the economics and workload and what we were doing.”
Silver recalled thinking that the idea made a lot of sense. And after a 10-minute meeting with YouTube CEO Susan Wojcicki, YouTube's first video chip project got the green light.
“It was very, very quick because it just made sense looking at the economics and workload and what we were doing,” Silver said.
Called Argos after the many-eyed monster in Greek mythology, YouTube first disclosed the chip to the public last year in a technical paper that boasted that the new design achieved a 20- to 33-fold increase in transcoding compute performance. Today Google has deployed the second-generation Argos chips to thousands of servers around the world, and has two future iterations in the works.
DIY SOCs
Google’s self-built YouTube chips are part of a growing trend among the tech giants. Amazon has built its Graviton server processors, Microsoft is working on Arm-based server processors, Facebook has a chip design unit — the list goes on.
A common assumption is that big tech companies are getting into chipmaking because it’s an obvious way to save money.
