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Hello, my name is
Alberto Villarreal.

In this short video,
I want to give you

an introduction to a new feature
in the Intel Xeon Scalable

Processors that is designed
to accelerate the learning use

cases.
Deep learning has gained
significant attention

in the industry by achieving
state-of-the-art results

in image classification,
speech recognition,

language translation,
object detection,

and other applications.
Second-generation Intel
Xeon Scalable Processors

led to increased performance
of deep learning applications,

from cloud to edge
devices, while using

the same hardware for many
other types of workloads.

This is because of new
features in these processors

such as Intel Advanced Vector
Extensions 512 or Intel

AVX-512, which is a
set of instructions

that can accelerate performance
for demanding computation

of tasks.
Intel AVX-512 now includes Intel
AVX-512 Deep Learning Boost,

which has new instructions
that accelerate deep learning

inference workloads such as
image classification, object

detection, and others.
Let's see how this
new technology works.

Research has shown
that both deep learning

training and inference
can be performed

with lower numerical
precision using

16-bit multipliers for
training and 8-bit multipliers

or fewer for inference with
minimal to no loss in accuracy.

The previous generation of
Intel Xeon Scalable Processors

enabled lower
precision for inference

using the Intel AVX
512 instruction set.

These instructions enable
lower-precision multiplies

with higher-precision
accumulates.

As shown in this figure,
multiplying two 8-bit values

and accumulating the
result of 32 bits

requires three instructions
with the accumulation

in Int32 format.
The new generation of Intel
Xeon Scalable Processors

now include Intel AVX-512
Deep Learning Boost,

which enables 8-bit multiplies
with 32-bit accumulates

with one single instruction.
The three instructions used
in the previous generation

are now fused into
the new instruction.

This allows for significantly
more performance

with less memory requirements.
We can use this new
functionality in several ways.

First, let me show you how to
take advantage of the Intel

AVX-512 Deep Learning Boost
via functionality available

in the Intel Math Kernel
Library for Deep Neural Networks

or Intel MKL-DNN.
Intel MKL-DNN is an
open-source performance library

for deep learning
applications intended

for acceleration of
deep learning frameworks

on Intel architecture.
It contains vectorized and
threaded building blocks

that you can use to implement
deep neural networks.

This is a good way to make
use of the deep learning

primitives that are
already optimized

to run on Intel processors.
You can simply use any of
the deep learning frameworks

or libraries.
Many are listed here
with more coming soon.

They use Intel
MKL-DNN to benefit

from the performance gains
offered by Intel Deep Learning

Boost.
You can also link your
application to Intel MKL-DNN

via C or C++ APIs.
This way, you can take advantage
of deep learning primitives

and performance-critical
functions

that are already optimized to
use Intel Deep Learning Boost.

This allows you to develop your
own optimized software products

or to optimize existing ones.
For example, let us suppose
we want to use the C++ API

in Intel MKL-DNN to implement
a convolution with a rectified

linear unit from the AlexNet
topology using lower-precision

primitives.
This diagram shows the
flow of operations and data

for this example.
Notice that we start
performing a quantization

step to get low-precision
representations

of data, weights, and biases
for the convolution layer.

Then we perform the convolution
operation using lower-position,

and at the end, the
output of the computation

is dequantized
from 8-bit integers

into the original
floating-point format.

The source code for
this example can

be found in the Intel
MKL-DNN repository.

You can go to the main
page in the repository

and click on the
SimpleNet example, where

you can find an introduction
to 8-bit integer computations,

including the quantization
process, which converts a given

input into a
lower-precision format.

On this page, you will find a
walkthrough of the source code

that implements the convolution
operation in this example,

showing the different steps
involved in implementation.

You can use this code
sample as a basis

to create your own network and
take advantage of the new Intel

AVX-512 Deep Learning
Boost functionality.

The complete source code
for this example, as well as

other examples, tutorials,
and installation directions

for Intel MKL-DNN
can be downloaded

from the GitHub repository
listed in the links section.

The code samples
that I just showed

illustrate how you can use the
new Intel AVX-512 Deep Learning

Boost feature to accelerate
your applications.

Of course, you can also take
advantage of these new features

by using frameworks and
libraries that have already

been optimized for Intel
AVX-512 Deep Learning Boost.

I hope this information
was useful for you.

Remember to check out the
links provided for resources

that you can use to make
your artificial intelligence

applications run faster.
Thanks for watching.
コツ:単語をクリックしてすぐ意味を調べられます!

読み込み中…

Boost Deep Learning with Intel Advanced Vector Extensions 512 | Intel Software

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alex 2019 年 4 月 26 日 に公開
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