BILLY RUTLEDGE: Hi, everyone.
I'm Billy Rutledge from the AOI team at Google.
And we're here today at CES in the NXP Pavilion
to introduce our new product-- the Edge TPU Dev
Board that features our edge TPU chip combined with the NXP iMX
8 SoC as a kit for developers to experiment
with artificial intelligence for the first time.
So the board itself is actually two pieces.
It's the base board here, which has all the connectors
that most developers would use to prototype a new product
idea.
And then the SoM module--
SoM-- includes the CPU, GPU, and TPU chip,
as well as the memory and Wi-Fi and Bluetooth.
And it actually snaps into the baseboard
using high-density connectors.
And so that allows you to experiment
with the actual hardware in a development setting,
but be able to buy the SoM part for production line
when you're ready to take your smart speaker,
smart dishwasher, smart TV to a scalable production plan.
So today we're showcasing a few different demos
of how you might experiment with this type of technology,
and hoping to inspire people to explore using AI on the edge.
PETER MALKIN: Hi, my name is Peter Malkin.
I work for Google.
I'm a software tech lead for AOI projects.
And today we're showing you a demo of facial detection
that runs on Edge TPU.
The key point about Edge TPU is the privacy and security.
From now on, your pixels do not need
to travel to a data center.
You do not need to contribute your data to any company.
You can run all your machine learning inference locally
on the chipset.
In this case in particular, we've
tried a network that can recognize human face.
And it's running locally on device
on a small embedded system that runs Linux.
JUNE TATE-GANS: Hi, my name is June Tate-gans.
I'm actually one of the software engineers
working on AOI projects.
One of our demos here at CES is actually a teachable machine
where we actually use local inference
to train a model directly on the device with no network
connectivity.
We call this our teachable machine demo,
and it's right here.
And essentially it has a camera pointing up at the sky.
Now the first thing I have to do is
train it to teach it about what the background is
so it can differentiate between the objects
I'm about to show it and what the background is.
And the first thing I do is I press one of these buttons
to actually tell it what it's looking at.
So now it knows what the background is.
I can now train it on an object.
In this particular case, I'm gonna use this ice cream.
So hold the ice cream over, press the button.
It now can differentiate between background and ice cream.
And you know it's machine learning and doing inference
because I can show it a different color
and get the same result. And this
can be extended to other objects as well, such as this hot dog.
So hot dog, ice cream, hot dog again.
And the same thing with a donut.
So donut, hot dog, and ice cream.
LEONID LOBACHEV: Hello.
My name is Leonid.
I work for Google for AOI project.
And I will talk about our [INAUDIBLE] demo here.
So if you notice that under each of the bigger demos,
we have a small display underneath with a depth camera.
And it shows the time.
And that's important characteristic
because it tracks how much time people spend looking
at the other bigger demos.
And you can notice that we display the bounding boxes
around the people's faces.
So here is my face.
And there is a green box showing that I'm
looking toward the demo stand.
And if I turn away, like right now you
can probably see the red box.
I'm not sure myself, but it should have been red.
And this has been run totally on the development board.
You can notice that, behind the display,
we have the same words like [INAUDIBLE] demos here.
So there is no internet or cloud connection required.
And that's a good application because we are ourselves
interest at home many people are looking at other bigger demos.
And you can notice like, in the middle,
we have something like 4 and 1/2 hours right now.
And in the corner stand there is like slightly more than three
hours.
It's quite explainable, but still interesting,
statistics together.
BILLY RUTLEDGE: So we're just beginning
to scratch the surface of what's possible with
artificial intelligence today.
And we're excited to offer the Google Edge TPU Dev
Kit for the world to experiment with on-device AI specifically
to explore the capabilities with high performance on device,
security with having all the data on the board itself,
and performance by being able to process everything locally
on the machine.
We think it will open up a world of opportunities
for new product development.
We're excited to see what you might build with it next.
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