What is Google Tensor? Everything you need to know

With the Pixel 6, we finally got hands-on with Google’s first bespoke mobile system on a chip (SoC), dubbed Google Tensor. While the company has dabbled with add-on hardware in the past, like the Pixel Visual Core and Titan M security chip, the Google Tensor chip represents the company’s first attempt at designing a custom mobile SoC. Or at least part-designing.

Even though Google hasn’t developed every component from scratch, the Tensor Processing Unit (TPU) is all in-house, and it’s at the heart of what the company wants to accomplish with the SoC. As expected, Google has stated that the processor is laser-focused on enhanced imaging and machine learning (ML) capabilities. To that end, Tensor doesn’t deliver ground-breaking raw power in most applications, but that’s because the company is targeting other use-cases instead.

Given this nuanced approach to chip design then, it’s worth taking a closer look at the guts of Google’s new SoC and what the company hopes to accomplish with it in the future. Here’s everything you need to know about Google Tensor.

What is an SoC? Everything you need to know about smartphone chipsets

What is the Google Tensor chip all about?

First and foremost, Tensor is a custom piece of silicon designed by Google to be efficient at the things the company most wants to prioritize, such as machine learning-related workloads. Needless to say, Tensor is a significant step up from the chips Google used in the previous-generation mid-range Pixel 5. In fact, it rubs shoulders with flagship SoCs from the likes of Qualcomm and Samsung.

That’s no coincidence, though — we know that Google collaborated with Samsung to co-develop and fabricate the Tensor SoC. And without delving too deep into the specifications, it’s also worth noting that the chip shares many of the Exynos 2100’s underpinnings, from components like the GPU and modem to architectural aspects like clock and power management.

Admittedly, a modest speed bump isn’t all too exciting these days and Google could have obtained similar performance gains without designing its own SoC. After all, many other smartphones using other chips, ranging from earlier Pixel devices to rival flagships, are perfectly fast enough for day-to-day tasks. Thankfully, though, there are plenty of other benefits that aren’t as immediately obvious as raw performance gains.

As we alluded to earlier, the star of the show is Google’s in-house TPU. Google has highlighted that the chip is quicker at handling tasks like real-time language translation for captions, text-to-speech without an internet connection, image processing, and other machine learning-based capabilities, like live translation and captions. It also allowed the Pixel 6 to apply Google’s HDRNet algorithm to video for the first time, even at qualities as high as 4K 60fps. Bottom line, the TPU allows Google’s coveted machine learning techniques to run more efficiently on the device, shaking the need for a cloud connection. That’s good news for the battery and security conscious.

See also: How on-device machine learning has changed the way we use our phones

Google’s other custom inclusion is its Titan M2 security core. Tasked with storing and processing your extra sensitive information, such as biometric cryptography, and protecting vital processes like secure boot, it’s a secure enclave that adds a much-needed additional level of security.

How does Google’s chip stack up against the competition?

We knew pretty early on that Google would be licensing off-the-shelf CPU cores from Arm for Tensor. Building a new microarchitecture from scratch is a much bigger endeavor that would require significantly more engineering res. To that end, the SoC’s basic building blocks may seem familiar if you’ve kept up with flagship chips from Qualcomm and Samsung, except for a few notable differences.

Unlike the latest flagship SoCs like the Exynos 2100 and Snapdragon 888, which feature a single high-performance Cortex-X1 core, Google has opted to include two such CPU cores instead. This means that Tensor has a more unique 2+2+4 (big, middle, little) configuration, while its competitors feature a 1+3+4 combo. On paper, this configuration may appear to favor Tensor in more demanding workloads and machine learning tasks — the Cortex-X1 is an ML number cruncher.

As you may have noticed, though, Google’s SoC skimped on the middle cores in the process, and in more ways than one. Besides the lower count, the company also opted for the significantly older Cortex-A76 cores instead of the better performing A77 and A78 cores. For context, the latter is used in both the Snapdragon 888 and Samsung’s Exynos 2100 SoCs. As you’d expect from older hardware, the Cortex-A76 simultaneously consumes more power and puts out less performance.

This decision to sacrifice middle core performance and efficiency was the subject of much debate and controversy prior to the Pixel 6’s release. Google hasn’t given a reason for using the Cortex-A76. It’s possible that Samsung/Google didn’t have access to the IP when Tensor development began four years ago. Or if this was a conscious decision, it may have been a result of silicon die space and/or power budget limitations. The Cortex-X1 is big, while the A76 is smaller than the A78. With two high-performance cores, it’s possible that Google had no power, space