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Hello everyone
My name is Malcolm
I'm very happy to be here to introduce
IEI's new AI inference acceleration card
V stands for VPU
MX8 means there are
8 Intel® Movidius™ Myriad™ MA2485
inside this PCIe card
With Intel OpenVINO™ toolkit
You can surf on deep learning inference acceleration
by Mustang-V100-MX8
Before we introduce the new product
Let's talk about why IEI designs this new product
As we know
IEI is a leading company of hardware design
and QNAP is one of IEI group companies
that has strong software capability
Due to the AI chip capability is getting stronger
and AI algorithm is getting higher accuracy than ever
we expect the AI demand
will increase dramatically in the future
That's why IEI cooperates with Intel
to design AI acceleration cards
and in the meanwhile
QNAP invests lots of resources to focus on AI industry
From factory inspection
smart retail to medical assistance systems
there are more and more AI applications
in our everyday life
Actually, AI is not going to replace human at all
we use AI to assist human because it's never get fatigue
and will not affect it's judgment
by other undesired factors
Let's have some recap of AI process
In traditional machine learning
engineer and data scientist
have to define the feature of input image.
For example in this case
we have to create the feature of ears shape
mouse shapes and tails appearance manually
then it can predict the input image
Deep learning progress
We have to prepare the tagged training image
Use the suitable topology to train the model
The features will be created automatically by the algorithm
In this case
deep learning starts to extract edge in the first layer
get bigger region such as nose
leg in deeper layer
and finally predict the input image
which means 90% dog in this case
The main differences between
traditional machine learning and deep learning are
machine learning have to highlight the feature manually
by engineer and data scientist
Deep learning will generate by topology itself
the second difference is the more correct data
will help deep learning to get higher accuracy
That's why in recent year deep learning methods
can almost reach human judgment
in the ImageNET contest
This page includes deep learning tool frameworks
Above OS
there is framework for deep learning
such as TensorFlow
and MXNet
Then use the suitable topology
to train deep learning model
Let's say for Image classification task
you may select AlexNet
For object detection task
you may choose SSD and YOLO
Deep learning has two sections
one is training
another is inference
The tag images generate trained model
by a desired topology
Then output the trained model into inference machine
to predict the result
The technology of training is almost well-established
in today's industry
but how to complete the inference process
is the most important task today
What is OpenVINO toolkit
OpenVINO is short of Open Visual Inference
& Neural Network Optimization
It's an Intel open source SDK
which can convert popular frameworks into
Intel acceleration hardware
The heterogeneous feature allows it
to work in different acceleration platform
such as CPU, GPU, FPGA, VPU
It also includes model optimizer
to convert pre-trained model
from different frameworks to Intel format.
Inference engine is a C++ API
by which you can call API for inference applications
Besides it also has optimized OpenCV
and Intel® Media SDK to do the code works.
Here is the OpenVINO™ toolkit workflow
In the first step it converts the frameworks
by model optimizer
Then start by the left top corner video stream
Because all video stream was encoded
it needs to be decoded by Intel media SDK
Then it needs some image preprocess
to remove background
or emphasize the image
by morphology methods in OpenCV
then call inference engine to predict the input image
After the prediction
it will need image post process
which means use OpenCV to highlight the object you detect
or add some text on the detect result
then the final step is to encode the video
then send it to other server.
OpenVINO™ toolkit includes many examples
and pre-trained models
such as object recognition
image classification
age-gender recognition
vehicle recognition
Which you can download and get familiar
with the OpenVINO toolkit interface
from Intel official website
In hospital there are many data
that can be analyzed by AI methods
Such as OCT and MRU images
and other physical data from patients
Let's take a look by one example.
Macular degeneration would happen in senior citizens
Once you found you have vision distortion
it might be too late for medical treatment
In this case, it uses ResNET
to train 22000 tag OCT images
by medical experts
And it can predict wet
dry, normal macular condition by AI method.
We can see from the left hand side picture
it is the deep learning application
without OpenVINO toolkit
the frame rate is about 1.5 fps.
In the right hand side picture, using OpenVINO toolkit
the performance is 28 fps
which is almost 20 times increasing in this application.
Here is the IEI Mustang acceleration card series
Including CPU FPGA and VPU, VPU
VPU means Mustang-V100-MX8
They are all based on OpenVINO toolkit
to implement on deep learning inference applications
The features of Mustang-V100-XM8
It's a very compact PCIe card
which has half-length single slot dimension
The power consumption is 30 W
which is extremely low
8 Intel® Movidius™ Myriad™ X VPU inside
provide powerful computation capability
It's ideal for edge computation.
Other features are
wide temperature rang for operating in
0~55 degree Celsius
and supporting multiple cards
It can also support popular topologies
such as AlexNet, GoogleNet, SSD and YOLO
another great feature
Mustang-V100-MX8 is a
decentralized computing platform
It can distribute difference VPU for different video input
and even different topology for each VPU
Which has very high flexibility for your applications
Mustang-V100-MX8 supports topology
such as AlexNet, GoogleNet
It's ideal for image classification
SSD, YOLO is suitable for object detection and applications
Here is a comparison table of
FPGA and VPU acceleration cards
which can help user to choose
what's the ideal card for their applications
VPU is an ASIC
which has less flexibility compared to FPGA
But with it's extremely low power consumption
and high performance
it's very suitable for edge device inference systems
Let's introduce IEI's AI acceleration cards roadmap
CPU and FPGA acceleration cards are already launched
and VPU acceleration card will launch in December
more and more SKU
such as mini PCIe
and M.2 acceleration cards interface will be ready soon.
Here we introduce an ideal
IEI system for AI deep learning
FLEX-BX200 is a 2U compact chassis
with rich I/O and can connect to FLEX PPC
to become a IP 66
high level water and dust proof system
which is ideal for the environment of traditional industry
TANK AIOT development kit
is an Intel proved OPENVINO toolkit platform
with pre-installed OpenVINO toolkit
It's an OpenVINO ready kit
it can develop your
deep learning inference applications
with IEI VPU FPGA acceleration cards right away
Here is an example
In this demo
we are using TANK-AIOT Development Kit
and the Mustang-V100-MX8 with OpenVINO toolkit
to run an Intel pre-trained model of vehicle classification
Let's start the demonstration
Here we have TANK-AIOT Development Kit
combining with our Mustang-V100-MX8
to process the OpenVINO pre-trained model
about the vehicle recognition
In this demonstration
it's using GoogleNET and YOLO to do the car detection
and vehicle model recognition
so you can see from the laptop corner
the frame rate is around 190 and 200
which means its car computation capability is extremely high
Mustang-V100-MX8 has the features about
very low consumption and also very compact size
which is an ideal acceleration card
for your edge computation device
That's today's demonstration
Mustang-V100-MX8 is an ideal acceleration card
for the AI deep learning applications
Hope you can understand more by today's introduction
and the demonstrations
If you have more question
please contact us
or scan the QR code to get more detail
Thank you. Bye.
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IEI Deep Learning Inference Acceleration Card

116 Folder Collection
alex published on April 27, 2019
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