The “Google Silicon” team gives us a tour of the Pixel 6’s Tensor SoC

Enlarge / A promo picture for the Google Tensor SoC.


The Pixel 6 is official, with a wild new digital camera design, unimaginable pricing, and the brand new Android 12 OS. The headline element of the system must be the Google Tensor “system on chip” (SoC), nonetheless. That is Google’s first important SoC in a smartphone, and the chip has a novel CPU core configuration and a powerful concentrate on AI capabilities.

Since when is Google a chip producer, although? What are the objectives of Tensor SoC? Why was it designed in its distinctive approach? To get some solutions, we sat down with members of the “Google Silicon” workforce—a reputation I do not suppose we have heard earlier than.

Google Silicon is a gaggle liable for cell chips from Google. Which means the workforce designed earlier Titan M safety chips within the Pixel 3 and up, together with the Pixel Visual Core within the Pixel 2 and three. The group has been engaged on important SoC growth for 3 or 4 years, but it surely stays separate from the Cloud workforce’s silicon work on issues like YouTube transcoding chips and Cloud TPUs.

Phil Carmack is the vice chairman and common supervisor of Google Silicon, and Monika Gupta is the senior director on the workforce. Each have been good sufficient to inform us a bit extra about Google’s secretive chip.

Most cell SoC distributors license their chip structure from ARM, which additionally presents some (non-obligatory) pointers on tips on how to design a chip utilizing its cores. And, aside from Apple, most of those customized designs stick fairly intently to those pointers. This 12 months, the most typical design is a chip with one massive ARM Cortex-X1 core, three medium A78 cores, and 4 slower, lower-power A55 cores for background processing.

Now wrap your thoughts round what Google is doing with the Google Tensor: the chip nonetheless has 4 A55s for the small cores, but it surely has two Arm Cortex-X1 CPUs at 2.8 GHz to deal with foreground processing duties.

For “medium” cores, we get two 2.25 GHz A76 CPUs. (That is A76, not the A78 everybody else is utilizing—these A76s are the “massive” CPU cores from final 12 months.) When Arm launched the A78 design, it stated that the core—on a 5nm course of—supplied 20 % extra sustained efficiency in the identical thermal envelope in comparison with the 7nm A76. Google is now utilizing the A76 design however on a 5nm chip, so, going by ARM’s description, Google’s A76 ought to put out much less warmth than an A78 chip. Google is principally spending extra thermal price range on having two massive cores and fewer on the medium cores.

So the primary query for the Google Silicon workforce is: what’s up with this core structure?

Carmack’s rationalization is that the dual-X1 structure is a play for effectivity at “medium” workloads. “We targeted quite a lot of our design effort on how the workload is allotted, how the vitality is distributed throughout the chip, and the way the processors come into play at varied deadlines,” Carmack stated. “When a heavy workload is available in, Android tends to hit it laborious, and that is how we get responsiveness.”

That is referring to the “rush to sleep” habits most cell chipsets exhibit, the place one thing like loading a webpage has the whole lot thrown at it so the duty will be completed shortly and the system can return to a lower-power state shortly.

“When it is a steady-state downside the place, say, the CPU has a lighter load but it surely’s nonetheless modestly important, you may have the twin X1s working, and at that efficiency degree, that would be the best,” Carmack stated.

He gave a digital camera view for example of a “medium” workload, saying that you simply “open up your digital camera and you’ve got a stay view and quite a lot of actually attention-grabbing issues are occurring suddenly. You’ve got obtained imaging calculations. You’ve got obtained rendering calculations. You’ve got obtained ML [machine learning] calculations, as a result of possibly Lens is on detecting photographs or no matter. Throughout conditions like that, you might have quite a lot of computation, but it surely’s heterogeneous.”

A fast apart: “heterogeneous” right here means utilizing extra bits of the SoC for compute than simply the CPU, so within the case of Lens, which means CPU, GPU, ISP (the digital camera co-processor), and Google’s ML co-processor.

Carmack continued, “You may use the 2 X1s dialed down in frequency so that they’re ultra-efficient, however they’re nonetheless at a workload that is fairly heavy. A workload that you simply usually would have completed with twin A76s, maxed out, is now barely tapping the gasoline with twin X1s.”

The digital camera is a good case research, since earlier Pixel telephones have failed at precisely this type of job. The Pixel 5 and 5a each recurrently overheat after three minutes of 4K recording. I am not allowed to speak an excessive amount of about this proper now, however I did report a 20 minute, 4K, 60 FPS video on a Pixel 6 with no overheating points. (I obtained bored after 20 minutes.)

This is what the phone looks like, if you're wondering.
Enlarge / That is what the cellphone appears to be like like, if you happen to’re questioning.


So, is Google pushing again on the concept one massive core is an effective design? The thought of utilizing one massive core has solely just lately popped up in Arm chips, in any case. We used to have 4 “massive” cores and 4 “little” cores with none of this super-sized, single-core “prime” stuff.

“All of it comes right down to what you are attempting to perform,” Carmack stated. “I am going to inform you the place one massive core versus two wins: when your aim is to win a single-threaded benchmark. You throw as many gates as potential on the one massive core to win a single-threaded benchmark… In order for you responsiveness, the quickest method to get that, and essentially the most environment friendly method to get high-performance, might be two massive cores.”

Carmack warned that this “may evolve relying on how effectivity is mapped from one era to the following,” however for the X1, Google claims that this design is best.

“The only-core efficiency is 80 % quicker than our earlier era; the GPU efficiency is 370 % quicker than our earlier era. I say that as a result of persons are going to ask that query, however to me, that is probably not the story,” Carmack defined. “I feel the one factor you possibly can take away from this a part of the story is that though we’re a brand-new entry into the SoC area, we all know tips on how to make high-frequency, high-performance circuits which might be dense, quick, and succesful… Our implementation is rock stable when it comes to frequencies, when it comes to frequency per watt, all of that stuff. That is not a cause to construct an all-new Tensor SoC.”

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