Youtube sometimes a great notion9/10/2023 One of the earliest and best-known examples is Google’s TPU, which it developed to tackle AI tasks in its data centers.įor certain workloads, “the TPU reduces the number of data centers they have to build by 50%,” Goldberg said. “Typically what that means is you have some software that you want to tie to the chip, and you get a big performance gain,” Goldberg said. The motive is actually pretty simple: The big tech companies are designing their own chips to create a strategic advantage. “We like saving money, but what we really want to do is deliver an as-good - if not a better - quality experience for viewers.” ![]() “Our focus is not really on saving money,” Silver said. A new advanced chip can cost hundreds of millions of dollars to simply build a prototype, which can then cost tens of millions of dollars to perfect. For one thing, the economics don’t make sense - it’s not worth the massive effort to hire and nurture chip designers to save a few dollars on the margin front. Most chip companies operate with a gross margin north of 50%, so by moving the chip design process in-house, tech companies can theoretically save an enormous amount of money.īut that’s not the case, according to Jay Goldberg, principal at D2D Advisory. 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.Ī common assumption is that big tech companies are getting into chipmaking because it’s an obvious way to save money. Google’s self-built YouTube chips are part of a growing trend among the tech giants. Today Google has deployed the second-generation Argos chips to thousands of servers around the world, and has two future iterations in the works. “It was very, very quick because it just made sense looking at the economics and workload and what we were doing,” Silver said.Ĭalled 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. And after a 10-minute meeting with YouTube CEO Susan Wojcicki, YouTube's first video chip project got the green light. Silver recalled thinking that the idea made a lot of sense. “It was very, very quick because it just made sense looking at the economics and workload and what we were doing.” He asked for about 40 staff members and an undisclosed dollar amount in the millions to make it happen, Silver 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. “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. 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. The conversion part is especially tricky, and requires powerful chips in order to do it efficiently.Īnd 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. 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. ![]() “And then we looked at the fleet, and we saw that transcoding was consuming a large fraction of our compute cycle.” “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. 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. But as Google was developing the TPUs, the company figured out that AI wasn’t the only type of computing it could improve.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |