Name: Bill Jenkins, Intel | Super Computing 2017
Uploaded: 2018-01-06T13:44:19.000Z
Duration: 8 min 31 s
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>> Narrator: From Denver,
Colorado, it's theCUBE.

and we're excited to have our next guest.

He's a Product Line Manager
for AI on FPGAs at Intel.

Nice to meet you, and
nice to talk to you today.

So you're right in the middle
of this machine-learning

AI storm, which we keep
hearing more and more about.

Kind of the next generation
of big data, if you will.

since the telecom
industry back in the 90s.

Using this combination of
software programming and FPGAs

The use of both hardware, as
well as software on top of it,

to open up the solution
stack, open up the ecosystem.

What of those things are
you working on specifically?

I really build first the
enabling technology that brings

Where Intel is trying
to provide that solution

from top to bottom to deliver AI products.

FPGAs are a key piece of that
because we provide a different

way to accelerate those
machine-learning and AI workloads.

Where we can be an
offload engine to a CPU.

We can be inline analytics
to offload the system,

So that's a pretty
interesting piece because

the real-time streaming data
is all the rage now, right?

How do you get it written to the database?

How do you get it into
the micro-processor?

That's different than even two years ago.

You didn't really hear
much about real-time.

I think it's, like I said,
it's evolving quite a bit.

The data scientists work to
figure out what topologies

they want to deploy and how
they want to deploy 'em.

But now, people are
building products around it.

And once they start
deploying these technologies

into products, they realize
that they don't want to

A lot of this evolution that
we're building is to try to

find ways to more
efficiently do that compute.

What we call inferencing,
the actual deployed

It's not about waiting two
seconds to start the processing.

You know, in an
autonomous-driven car where

someone's crossing the
road, I'm not waiting

So I need to be able to
do that with video feeds,

right off a disk drive, from
the ethernet data coming in.

I want to do that directly
in line, so that my processor

can do what it's good at,
and we offload that processor

specifically, 'cause that is all the rage.

But there's also continuous
learning over time,

that's not necessarily dependent
on learning immediately.

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Bill Jenkins, Intel | Super Computing 2017

process

figure

evolve

specifically