BILL LUAN: Good afternoon, everyone.

Welcome to the Google I/O session of Introducing Google

Coral.

My name is Bill Luan.

I'm a program manager at Google's Coral team.

On behalf of all my team members,

warm welcome to all of you and many

of you watching online around the world.

Thank you for joining us.

Before I start to talk about the Coral product,

let's take a quick look at the industry

trend, which demonstrates to us why

Coral will be important to us.

The growth of the smart devices and the so-called IoT--

the internet of things--

has grown tremendously over the past number of years.

And it represents one of the biggest growth opportunity

in the years to come.

Per many industry trend forecasts over the internet,

such as this one you can see, you

can see many of those similar on the internet.

The growth of the non-IoT devices, such as PCs, laptops,

mobile phones, tablets, and so on, in the next five to six

years will grow from about 11 billion installed units

globally to around 12 billion over the next five to six years

time span, representing about 14% growth rate.

However, the IoT smart devices, the growth

rate from the current install base about 8 billion units

worldwide will grow to 21 billion in the same period,

representing a much larger growth rate of more than 150%.

So this really tells us where the growth opportunity

and opportunities for innovation for all of us,

for developers around the world, where that lies ahead.

Namely, they are in the smart devices.

So in the smart devices users continue

to grow the interest in smart devices in terms of innovation,

and the development will continue to grow.

There are several key factors which

will drive this trend continue forward.

Because number one, due to the increase

of the interest around the world in terms of AI,

machine learning, the advancement

of key research in AI continues to grow

over the last several years.

In fact, the number of research papers

has published in the last few years

in machine learning is more than the total number of such papers

in the last decade combined.

So more AI capabilities will make the application

of AI machine learning on devices more practical,

feasible, as machine learning models become

more accurate, faster, performance better,

industries will have an interest to continue to use them.

So as more devices at the edge require machine learning,

we really need to have a solution

to bring the machine learning onto the device at the edge.

We need a technology, especially a hardware,

to bring the machine learning acceleration, that capability

right on the device.

So in a nutshell, smart devices growth demands

bring machine learning to the edge to the device.

And we need a solution for that.

So let me introduce to you what Google has made for you--

for developers around the world--

to answer this industry demand what will make that possible.

Introducing Coral from Google.

It is a new exciting technology platform

to enable developers all around the world

to build on-device machine learning acceleration AI

applications with ease and with high efficiency.

So with this introduction session what you will learn

is about what the Coral product line offers.

What are machine learning capabilities

you can build with the Coral platform and the technology.

And third, what are the use cases in terms

of deploying machine learning with applications

in a lot of industry use.

So I'm going to go through all these with my instruction here.

Coral is designed as a-- make it easier

to bring machine learning on the device,

make it easier for you to develop applications.

It is a platform to make prototyping tool production

with high speed, with efficiency.

It is a platform to develop on-device machine learning.

It offers a group of components of hardware components

to bring unique high-performance machine learning capability

right onto the Edge devices.

It also offers a complete set of software tool change

to enable you to develop applications

in AI machine learning with ease.

In addition to that, Coral also offers a set

of ready-to-be-used machine learning models for you

to quickly deploy onto devices.

So a lot of you may curious.

Why it is called Coral?

How that has anything to do with AI?

Well, if you look at the nature of coral in the natural world,

coral really do represent a community of vibrant,

teeming with life, inclusive, very open community.

Right?

The living organism together, they're

working together to contribute to a common good.

And that's exactly what we inspire,

want to make a platform for the industry, for all of you.

Coral's inspiration and mission is

to provide a vibrant platform for all of you

to collaborate and to develop applications to bring

AI applications to the device.

We want to enable developers everywhere

to turning AI ideas to business solutions from idea

to prototyping to large scale production deployment

with ease and simplicity.

And finally, we want to encourage everybody joining

a community-wide effort to contribute and to learn

and to share machine learning models together.

So that's where the name of the Coral came from.

So why do we want to talk about the benefits of the machine

learning on device?

So let's take a quick look at what

are the key points of machine learning

on device in terms of benefits.

Number one, it is a high performance benefit.

Because everything is computed on the device right

on the device locally there.

You do not need to send the data back to the cloud.

Right?

So high performance, everything on the local

allow you to do things much more efficiently.

Also it's very important-- very key-- in many applications,

you want the data to stay on the device,

especially in business application scenarios.

Some user privacy data cannot be sent to a server,

cannot be sent to the cloud.

We want a technology to enable you

to make that solution available to your customers.

And also because data stays local.

All the information coming from the sensors right on the device

can be accessed and used to compute

for your machine learning results right there, rather

than you have to send the data to the cloud, to the server,

to compute and send the data back.

Right?

So this is another way to look at it.

It's a much better performance.

Because auditing every data is locally available.

Next is works offline.

There's many scenarios-- the internet of things,

the smart devices--

they may or may not have internet connection.

In fact, in most cases they don't have a cloud connection.

So you still want to have a machine learning

capability in that scenario.

And a offline on-device machine learning capability

will make that happen for you.

And lastly, it is much more power efficient.

Lot of Edge devices are small.

They don't have large power supply.

And they require high efficiency in terms of computing

computation on the device.

Because you don't have to send the data back to the cloud,

and of course, you save the bandwidth.

You save the power to send the data so use a Wi-Fi on network.

So the benefits of machine learning

is very strong on-device, machine learning very strong.

So let's take a quick look at the Coral product line.

What do we offer to the industry?

Coral product line offers both hardware components

and software components.

On the hardware side it offers development

on board, which is a single border computer allow you

to prototype developer applications.

Also, Coral offers a number of sensors

to allow you to build applications using

the sensory data in terms of imaging,

video, environmental sensors, making

those available as a part of the input of data

to your application.

And also in the software side, we

provide a complete set of software tool change

from operating system to SDK to machine

learning modules allow you to easily use them and quickly

build your application.

In addition to that, we provide a detailed comprehensive set

of contents in terms of documentation, examples,

online guide, data Sheets, et cetera.

All of those were made available online at the Coral website

to all of you.

So now we know what to the Coral product suite contains.

Let's take a look little bit more detail

in terms of the hardware components.

So Coral hardware product.

We offer a suite of hardware to you

in terms of prototyping and developing applications.

The first one is called Coral Dev Board.

As you can see on the picture here,

it retails for about $150.

It's a single-board computer with operating system

on board and machine learning capability right on the device.

The second one is a USB key, what we call USB accelerator.

It has the Edge TPO machine learning acceleration chip

right on the device.

You can put this USB into any Linux machine

and to be able to bring machine learning capability right

on to those devices.

And I have those with me.

And I want to show you the relative size

dimension of that.

So this is the Coral Deb Board.

It's very small, as you can see.

It is a single board computer with all

the necessary input/output of the connectors on that.

This is the USB key, it's even smaller.

It's just typical to any USB key you would use.

So these are the two current computer platforms

you use to develop applications.

In addition to that, we also offer a couple of sensors,

as I said, to take sensing data from the field for your machine

learning application to consume.

Number one is a five-megapixel autofocus camera,

what we call a Coral camera.

Second, we just released a few days ago

a new environmental sensor, which is this one here.

As you can see, very small.

And it has a digital display on that.

It allow you to take the input from temperature,

humidity, and light and so on.

These input sensors, you can use that and build it

for your applications.

Now with these you do the prototype.

But when you deploy these devices

into largest scale production environment,

we allow you to enable you to take the sun

module from the Dev Board.

In other words, this piece of circuit board can snap off.

You can embed into your product.

And this is for volume large scale deployment.

And the individual unit price, as you can see, for about $115.

And we offer discount for volume deployment.

Very soon in the next quarter we will offer a PCI-E connector--

a base connector-- which you can plug into any PC or industrial

PC and industry devices that accept a PCI-E

connector to allow you to bring machine learning capability

into those devices.

So those on the left, are what you

need to do prototyping for your machine learning application.

The one on the right--