Hours Later, The Ukrainians Blew It Up-With Artillery. Russia Announced It Had Deployed, To Ukraine, Its Best New Artillery-Detecting Radar. Final note, all Pixel devices are running the latest version of Android 12 while the others are still on Android 11, which may represent a small performance difference. Additionally, I turned off benchmark boosting mode on the ASUS and Snapdragon Insider devices. Also, the ZTE/Nubia RedMagic 6S Pro has an active cooled fan that automatically runs whenever graphically intensive applications are open, so it did turn on the fan for the 3DMark and PCMark tests but not for the GeekBench results. All benchmarks were run at least 3 times to normalize scores for any kind of variance except for 3DMark Wildlife Stress Test since it runs a 20-loop test. After that, I wanted to run something that was more of a system-level benchmark that considers all the different types of usages a user might have so I ran PCMark for Android. I also chose to use the stress test because thermal throttling is a real problem with some mobile SoCs and sustained performance is more relevant than a 60 second benchmark. 3DMark was important because it is cross-platform and the standard for 3D graphics performance, especially in gaming. This feels like the perfect test to use to compare AI performance across devices, especially since it does multiple types of ML workloads and tests FP32, INT16 and INT8 performance. The important part in choosing GeekBench ML was not to test CPU or GPU performance individually, but instead to test them together using the NNAPI test which is Google’s own API that it uses for ML acceleration. And when you consider how few AI benchmarks are out there and ones that are easy to run, GeekBench ML was an easy decision. To Google’s point, the company has said that it does not actually care about individual SoC component tests but rather a complete system AI test. I chose GeekBench because it’s a simple CPU benchmark and can show how Google’s decision to go with two Cortex X1 cores and two A76 cores instead of one X1 and three A78s affected overall CPU performance. Google claimed that nobody in the market was creating chips that satisfied Google’s needs for AI performance, so they created their own.įor my testing, I ran GeekBench, GeekBench ML, 3DMark Wildlife, 3DMark Wildlife Stress Test and PCMark. Nevertheless, Google’s intentions with the Tensor SoC is to derive some of the AI performance and intelligence that it has created with the TPU and bring that down into a mobile SoC. It seems odd that Google would try to claim the SoC as their own even though Samsung is heavily involved in the manufacturing and modem and likely some of the chip design as well. Many people believe that Google’s Tensor SoC is more akin to a Samsung Exynos semi-custom design and according to some code that Anandtech’s Andrei Frumusanu found, it probably is along those lines. But now the embargo for reviews has lifted and I’ve had an opportunity to test the Pixel 6 and Pixel 6 Pro’s tensor SoC. We recently covered some of the details around Google’s Tensor SoC at the launch of the Pixel 6 and Tensor SoC that Google had.
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