What is Google's Tensor chip? Everything you need to know
Tensor is Google's first ever attempt at building a custom SoC — here's why that is significant.
By Calvin Wankhede
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 the Google Tensor chip all about?
Google
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.
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 resources. 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.
CPU
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Google Tensor:
2x Arm Cortex-X1 (2.80GHz)
2x Arm Cortex-A76 (2.25GHz)
4x Arm Cortex-A55 (1.80GHz)
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Snapdragon 888:
1x Arm Cortex-X1 (2.84GHz, 3GHz for Snapdragon 888 Plus)
3x Arm Cortex-A78 (2.4GHz)
4x Arm Cortex-A55 (1.8GHz)
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Exynos 2100:
1x Arm Cortex-X1 (2.90GHz)
3x Arm Cortex-A78 (2.8GHz)
4x Arm Cortex-A55 (2.2GHz)
| GPU
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Google Tensor:
Arm Mali-G78 MP20
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Snapdragon 888:
Adreno 660
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Exynos 2100:
Arm Mali-G78 MP14
| RAM
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Google Tensor:
LPDDR5
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Snapdragon 888:
LPDDR5
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Exynos 2100:
LPDDR5
| ML
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Google Tensor:
Tensor Processing Unit
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Snapdragon 888:
Hexagon 780 DSP
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Exynos 2100:
Triple NPU + DSP
| Media Decode
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Google Tensor:
H.264, H.265, VP9, AV1
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Snapdragon 888:
H.264, H.265, VP9
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Exynos 2100:
H.264, H.265, VP9, AV1
| Modem
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Google Tensor:
4G LTE
5G sub-6Ghz & mmWave
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Snapdragon 888:
4G LTE
5G sub-6Ghz & mmWave
7.5Gbps download
3Gbps upload
(integrated Snapdragon X60)
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Exynos 2100:
4G LTE
5G sub-6Ghz & mmWave
7.35Gbps download
3.6Gbps upload
(integrated Exynos 5123)
| Process
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Google Tensor:
5nm
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Snapdragon 888:
5nm
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Exynos 2100:
5nm
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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, or thermal budgets left to include the newer A78 cores.
While the company hasn’t been forthcoming about many Tensor-related decisions, a VP at Google Silicon told Ars Technica that including the twin X1 cores was a conscious design choice and that the trade-off was made with ML-related applications in mind.
As for graphics capabilities, Tensor shares the Exynos 2100’s Arm Mali-G78 GPU. However, it is a beefed-up variant, offering 20 cores over the Exynos’ 14. This 42% increase is a rather significant advantage once again, in theory anyway.
How does the Google Tensor chip perform?
Jimmy Westenberg / Android Authority
Despite some clear advantages on paper, if you were hoping for generation-defying performance, you’ll be a bit disappointed here.
Google Pixel 6 Series Benchmarks
GeekBench 5 - CPU3DMark (Wild Life) - GPUSpeed Test G - System
GeekBench 5 - CPU
iPhone 13 Pro M…iPhone 13 (Apple…iPhone 12 Pro M…Samsung Galaxy…Samsung Galaxy…OnePlus 9 Pro…Samsung Note 2…Google Pixel 6 Pr…OnePlus 8 Pro…Google Pixel 6…Samsung Galaxy
Geekbench 5 - Single Core
Geekbench 5 - Multi Core
Higher scores are better for GeekBench 5 and 3DMark, lower is better for Speed Test G.
Note: See linked article for details
androidauthority.com
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