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Want to know who wins when we pit the Google Tensor vs Snapdragon 888 in a head-to-head processor showdown?
Google Tensor vs Snapdragon 888: How the Pixel 6
Chip is Performing The phone hasn’t even hit the market yet, but the prospect of a Pixel 6 series with a custom Google Tensor SoC already raises some big questions. Catch apple? Are you going to be using the latest and greatest technology out there?
Google could have bought chipsets from its partner Qualcomm or even bought an Exynos model from its new friends at Samsung, but that wouldn’t have been that fun. Instead, the company worked with Samsung to develop its own chipset that uses a combination of off-the-shelf components and some of its internal machine learning (ML) silicon.
According to a solid report, the Pixel 6’s Google Tensor SoC will look a little different from other flagship chipsets on the market. Of course, we save the benchmarking and all performance and battery ratings when we pick up the device, but we already have a lot of information to make a paper-based comparison between the latest Qualcomm chipsets (and Samsung even while we’re at it? Let’s take an early look at how the showdown between the Google Tensor vs. Snapdragon 888 chipset is playing out.
Google Tensor vs. Snapdragon 888 vs. Exynos 2100
While Qualcomm and Samsung’s next-gen SoCs are not far behind, Google is the one Tensor chip is designed to compete with the current generation of the flagship chipsets Qualcomm Snapdragon 888 and Samsung Exynos 2100. Use this as the basis for our comparison of its latest Exynos processor. According to the report, the modem and GPU settings were taken directly from the Exynos 2100, and the similarities extend to supporting similar AV1 media decoding hardware.
If the GPU configuration matches Samsung’s Exynos 2100, the Pixel 6 will also be a decent gaming phone, albeit a few frames behind the graphics capabilities of the Snapdragon 888. However, we expect the Pixel 6’s performance from the Tensor Processing Unit des Chip (TPU) will offer even more competitive capabilities for artificial intelligence and machine learning.
Google’s 2 + 2 + 4 CPU configuration is a stranger design choice. It’s worth exploring a bit more, which we’ll get to later, but the highlight is that two powerful CortexX1 CPUs are supposed to give the Google Tensor SoC more growl. Single-threaded, but older CortexA76 cores can make the chip a weaker multitasker. It’s an interesting combination that goes back to Samsung’s unfortunate Mongoose CPU setups. However, there are still major questions to be answered about the energy and thermal efficiency of this construction.
On paper, the Google Tensor processor and Pixel 6 series seems very competitive with the Exynos 2100 and Snapdragon 888, which can be found in some of the best smartphones of 2021.
Understanding Google’s Tensor CPU Layout
Let’s get to the big question on the lips of all enthusiasts: Why should Google choose the 2018 Arm CortexA76 CPU for a state-of-the-art SoC? The answer lies in a compromise in area, performance and heat.
I dug up a slide (shown below) from a previous display by Arm that helps visualize the key arguments. The graphic scale is admittedly not particularly accurate, but the bottom line is that the CortexA76 is smaller and less powerful than the newer CortexA77 and A78 with the same clock rate and manufacturing process (ISO comparison). has been working with Arm on a 5nm CortexA76 for some time. If you want numbers, the CortexA77 is 17% larger than the A76. while the A78 is only 5% smaller than the A77.Likewise, Arm managed to reduce power consumption by just 4 between the A77 and A78, so the A76 remained the smallest and lowest-performing option.
The tradeoff is that the CortexA76 offers much lower peak performance. Comparing Arm’s numbers, the company saw a 20% increase in microarchitecture between the A77 and A76 and another 7% in a similar process with the move to A78. As a result, multithreaded tasks can be slower on the Pixel 6 than on its Snapdragon 888 competitors, although that depends a lot on the exact workload, of course. With two CortexX1 cores for heavy lifting, Google can be sure your chip has the right chip. Combination of maximum performance and efficiency.
