IEI Tank and Up Squared AI Vision Development Kit | IOT Developer Show | Season 2 | Intel Software


Hello. Welcome to the IoT
Developer Show. I’m Martin Kronberg. In this series, we take a deep
dive into the OpenVINO Toolkit. This time, we
discuss got hardware for running your computer vision
applications, the IEI Tank AIoT Developer Kit, and the UP
Squared AI Vision Development Kit. Both of these kits
have the ability to run the OpenVINO Toolkit
right out of the box. [MUSIC PLAYING] This is Intel’s professional
grade IEI Tank AIoT Developer Kit. Let’s take a look at both
the hardware and software that’s included. The first thing that
you might notice are these massive heat sinks
at the front of the unit. This is due to the fact
that the system is designed to run completely fanless. A fanless design, when paired
with a ruggedized case, means that the system is perfect
for industrial deployments where dust can wreak havoc
on fan-cooled systems. Industrial deployments can also
have somewhat extreme ambient temperatures, and
this kit is rated to operate between
negative 20 to 60 degrees Celsius, or negative 4 to
140 degrees Fahrenheit, provided there’s enough airflow. The heat sink can also be used
as a great backup instrument for your future synth band. [STRUMMING HEAT SINK] Around the other side is
another interesting feature of the Kit– the I/O interfaces. In addition to the standard
USB, VGA, and HTMI outputs, we have a bank of serial ports. So that’s a lot of connectivity
to additional protocols right out of the box. We have 2 gigabit ethernet
ports with a power over ethernet card behind them. And this means
that you can drive a pair of ethernet cameras
from just these two ports. Now let’s take a look
at what’s inside. The kit comes with either a
6th Generation Intel Core i5 Processor, or an Intel
Core i7 Processor, 8 gigabytes of DDR4 RAM, and
a one terabyte hard drive– more than enough power
to handle multiple video streams if you want to run this
kit as an AI Vision processing hub. Overall, this is a
great piece of hardware to develop and deploy
professional industrial grade AI Vision applications. In addition to the
hardware itself, the kit also comes preloaded
with all the software that you need. The kit has the latest version
of the OpenVINO Toolkit at the time of
manufacture, so you should check for
software updates before beginning development. This developer kit also comes
with Intel System Studio 2018, which is
prepacked with VTune, so you can optimize
your applications. Let’s take a quick
look at how you can use these tools to find
hotspots in your application and understand the process load
of each neural network layer. Here is Intel VTune Amplifier. I have just run an analysis
on an object detection application of a 30-second video
using the MobileNet SSD model. I chose the advanced
hotspots analysis type in order to identify
which parts of my code are taking up the
most processing time. Let’s take a look
at what we found. Here we see three
main categories– OpenCV, the Inference
Engine, and Unknown, along with the CPU time of each. We can expand each of
these to get more info. Under Unknown, we see that there
are a lot of various functions. This one, for instance,
deals with accessing the video encoder
FFMpeg Library. In OpenCV, we can see
CPU time broken down by various functions, like
resize and video capture. And under Inference Engine is
the most interesting feature to me of this tool. Here you can see the
CPU time of each layer of your neural
network, as well as the details about the
subprocesses of that layer. Finally, down here, we
see a timeline of CPU load across all threads– really useful to
spot bottlenecks. With such an in-depth
analysis of CPU utilization, you can really begin to
optimize your applications. Intel has also partnered with
UP to release the UP Squared AI Vision Development Kit. As you can see, it’s much
smaller than the IEI Tank AIoT Developer Kit and really
meant for a smaller workload. It has all the same software
stack installed on it, but with somewhat lower specs. The kit is powered by an
Intel Atom x7 processor CPU with an onboard HD505 GPU. This combination gives you a lot
of performance in a low power envelope. The kit also has 4
gigabytes of DDR4 RAM and 64 gigabytes of
eMMC storage on board. It also comes with
an HD USB camera. And finally, on top, you
can see a PCIe expansion slot that has a card installed. This is the Intel
Movidius Myriad 2 VPU, or Visual Processing Unit. This is an accelerator
specifically designed to run vision inference
models in a power constrained environment. So this kit has three
different processing units– a CPU, a GPU, and a VPU. Leveraging multiple
types of processing units in a single application is
called heterogeneous computing. It’s a key method in creating
the most efficient runtime environment, and it’s
all about finding a balance between
flexibility and performance per watt of your
processing units. Using this kit and
the OpenVINO Toolkit, you can easily develop
at heterogeneous workflow for your AI vision applications. Let me show you a quick
example of how easy it is to set up a basic
heterogeneous execution. Here I am running the
interactive face detection sample. I’m loading multiple
models, and I’m specifying what processor to
run each on with a -d flag. Here I am using an onboard
GPU for three of the models and a CPU for one. It’s really simple to
hand off various workloads to different processing
units, and can lead to increased
overall performance of your application. So there you have it. Two kits that offer
a great solution for both entry level and
industrial IoT application development. Thank you so much for watching. Follow the links to learn
more about the kits, and I’ll see you
next time, when I’m going to talk about inference. [INTEL THEME PLAYING]

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