Rick: Google Tensor is the biggest mobile
hardware innovation in the history of the company.
It's the culmination of years of investment in AI,
and Google's deep experience in silicon.
The name is a nod to tensors,
a building block of machine learning computation.
The Tensor name also connects it to Google technologies like TensorFlow,
our open-source AI and ML software library,
and our Tensor Processing Units, which Google developed to power machine
learning in our data centers.
The Tensor chip is specifically designed
to offer Google's latest advances in AI directly on a mobile device.
This is an area where we've been held back for years,
but now we're able to open a new chapter
in AI-driven smartphone innovation.
Tensor also gives us a hardware foundation
that we'll be building on for years to come,
so you get the personal, helpful experiences
you'd expect from a Google phone.
Monika is here to explain what's so different about Tensor
Monika: Every couple of years,
Google comes out with something that completely changes
how people use technology in their lives.
We started in Search, and kept going with Google Translate,
Google Photos, Assistant,
and these innovations are built around our Machine Learning research.
It's in Google's DNA and drives everything we do.
While Google is known for ground-breaking work in ML,
there's one place we haven't always been able to bring it,
and that's a smartphone.
Mobile chips simply haven't been able to keep pace with Google research.
And rather than wait for them to catch up,
we decided to make one ourselves.
We needed a chip that was engineered
to fulfill our vision of what should be possible on Pixel.
So, a few years ago, Google's team of researchers
came together to collaborate across hardware, software, and ML.
The result of that work is Google Tensor!
We approached Tensor differently.
Every aspect of Tensor was designed and optimized
to run Google's ML models; this permeates our entire chip.
We're fortunate to have great insights when it comes to ML
and built our chip based on where ML models are heading,
not where they are today.
Starting with the integrated ML engine, the TPU.
It was custom made by Google Research for Google Research.
For the image signal processor, or ISP,
we brought key algorithms directly into the silicon for power efficiency.
Even our choices for CPU and GPU were designed to complement our ML
to deliver advanced computational photography.
The CPU cluster is a 2+2+4 configuration;
It includes two big ARM X1 cores;
The 20-core GPU also delivers a premium gaming experience
for the most popular Android games.
There's a context hub that brings machine
learning to the ultra-low power domain.
It enables ambient experiences like Now Playing
and Always-on-display to run all the time, without draining your battery.
Tensor was designed for total performance and efficiency
when it comes to running Google experiences.
It has to be really good at heterogeneous computing.
Here's what that means...
As software applications on mobile phones become more complex,
they run on multiple parts of the chip.
This is heterogeneous computing.
To get good performance for these complex applications,
we made system-level decisions for the SoC.
We ensured different subsystems inside Tensor work really well together,
rather than optimizing individual elements for peak speeds.
Peak CPU and GPU speeds look great in benchmarks,
but they don't always reflect real-world user experience.
Pixel 5 is a good example of our approach.
Google software delivered a great experience,
even on a chip that didn't win on benchmarks.
Don't get me wrong, Tensor's CPU and GPU
are much faster compared to any past Pixel.
But what matters more are the brand-new experiences
enabled by Google Tensor that weren't possible until now.