![]() ![]() ![]() 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’d also note this may not be official, so we’d take the benchmark with a pinch of salt, at least for now.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. However, these scores are still impressive and sit more towards the high-end range, but has been beat out by the iPhone 12 Pro Geekbench benchmark, which scored 1588 in the single-core score. Since this is a pre-release benchmark it’s possible that the Pixel 6 Pro will score differently when using the handset’s final software and hardware. The benchmark also gives the smartphone a single-core score of 1034 and a multi-core score of 2756. The four cores should be better used for tasks that don’t require a lot of power, which should help the Pixel 6 Pro save energy. It lists four cores sitting at 1.80GHz, alongside two cores at 2.25GHz and two cores at 2.80GHz. The benchmark also claims the Pixel 6 Pro will feature an ARM processor with eight cores. Unsurprisingly, the operating system for the Pixel Pro 6 is Android 12, which is an upgrade to the Google Pixel 5, that ran on Android 11.
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