Coral — What is it?

The Coral Edge TPU provides a means to perform advanced machine learning tasks in a low power, small form factor package. This hardware is based on an application specific IC (ASIC) tailored for hardware accelerated AI calculations.

This TPU is appropriate for an application where identifying an object or pattern is required. This could be, but is not limited to:

• Object detection
• Pose or gesture estimation
• Image segmentation
• Key phrase detection

Some practical applications are:

• Autonomous vehicles
• Robots
• Voice control/Language Processing
• Monitoring devices

Nearly all industries can benefit from this technology. To name a few more specifically:

• Health Care
• Agrictuture
• Manufacturing
• Oil and Gas
• Security/Defence
• Automated Kiosk

For more information about the hardware see these references:

• ​https://coral.ai
• ​https://en.wikipedia.org/wiki/Tensor_processing_unit

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Getting started with the Coral Edge TPU

To begin you will need:

• A workstation with Linux natively installed.
• A Newport SBC — Coral requires AARCH64.
• A Coral EDGE TPU, our testing was done with the mPCI-e form factor model.
• Network/Internet connection.
• Optional: USB webcam

Compiling the kernel

The Gateworks kernel defconfig for Newport does not include support for video devices. For the sake of convenience a pre-built image is available for download. If you would like to create a similar image manually:

• Acquire the Newport BSP, we will call the directory this repo has been sync'ed to the <BSP> directory.
• Follow the steps here to modify the kernel and create and Ubuntu image.
  • In the menuconfig enable the module "USB_VIDEO_CLASS", this will allow you to use a USB webcam with v4l-utils.
  • Complete the procedure detailed in the aforementioned section, build your Bionic image and flash it to your SBC

Building and installing the Gasket and Apex modules

Video: ​https://youtu.be/PcrGUiuNBcg

The source code for the modules can be downloaded here as a tar.gz file:

• ​https://coral.googlesource.com/linux-imx/+/refs/heads/release-day/drivers/staging/gasket
  • Extract this tar into a directory, this dir will be referred to as <the_module_directory>.

Build the source using the same method as you would for an out of tree module (out of tree = when the module source is not included in the kernel source).

• cd to the <BSP> directory.

• source setup-environment 
  

Doing this will configure the toolchain for building. Keep in mind some of the lines in this file use the argument $PWD thus it should not be sourced from any location other than the <BSP> folder.

• You can verify the arguments have been exported by executing the command

  echo $ARCH
  

This will return "arm64"

• cd to <the_module_directory>
• cat the "Makefile", you'll see two variables which need to be set for the modules to build correctly.

  export CONFIG_STAGING_GASKET_FRAMEWORK=m
  export CONFIG_STAGING_APEX_DRIVER=m
  

• Execute the following command

  make -C <BSP>/linux M=$PWD
  

This procedure will result in two modules being created, "apex.ko" and "gasket.ko". Copy the .ko files to your target board "/lib/modules/<kernel_version>/extra/" folder. Using SCP may be the simplest way to go about this.

With the modules copied to the board execute the following commands:

depmod -a

insmod gasket.ko
sync

Remove power from the board and reboot. On reboot verify that "/dev/apex_0" device is present.

Installing and configuring Python

Python 3.7 is required to run the tenser flow examples. Other versions can be used, though at the time of writing this wiki 3.7 is the best option when using the Bionic Ubuntu for Newport BSP.

apt-get update
apt install python3.7 -y

Set Python 3.7 to have priority over 3.6.

update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 1
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 2  

update-alternatives --config python3
#on this menu enter the number 2. 

You can verify you have been sucessful in changing the version with the following command:

python3 --version

Installing the TPU runtime

Install Curl

apt-get install curl -y

Add Debian package repository to your system

echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
sudo apt update

Install PIP and required libraries

apt-get install python3-pip libedgetpu1-std -y

pip3 install --upgrade pip setuptools wheelpi

Download the TFlite Runtime .whl

wget https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_aarch64.whl

*Note:* If you have chosen not to use Python 3.7 you can find the .whl appropriate for your version here ​https://www.tensorflow.org/lite/guide/python

Acquire necessary libraries to build the TFlite runtime

pip3 install cython
pip3 install numpy
apt-get install python3-pil

Install TFlite runtime

pip3 install tflite_runtime-2.1.0.post1-cp36-cp36m-linux_aarch64.whl  

Download Classification example, test an inference operation

Create a place for the Coral examples to reside.

mkdir coral && cd coral

Clone the examples, "cd" to examples directory.

git clone https://github.com/google-coral/tflite.git
cd tflite/python/examples/classification

Install prerequisite programs using supplied script.

bash install_requirements.sh

Run the classifcation demo.

python3 classify_image.py \
--model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
--labels models/inat_bird_labels.txt \
--input images/parrot.jpg  

Gstreamer example

This example will use a USB webcam and the TPU to identify objects presented to the webcam. The video output and overlay will be streamed to a location on the network for viewing.

Install Gstreamer, you will need this program on both the SBC and your workstation where you will be viewing the output.

apt-get install gstreamer1.0-x gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-alsa -y

Clone the Gstreamer example

mkdir google-coral && cd google-coral
git clone https://github.com/google-coral/examples-camera.git --depth 1

Download models. Models are the information that will be feed to the TPU for it to reference when identifying an object.

cd examples-camera
sh download_models.sh

Configure the Gstreamer Python script

cd gstreamer
bash install_requirements.sh

Adaptations to the existing gstreamer.py will be required for this example to work. A modified script is available to download from ​here. Edit this file line 231 with the IP address of the desktop workstation you will be streaming to. This is what the edited line will look like.

  ! rsvgoverlay name=overlay ! videoconvert ! jpegenc ! tcpclientsink host=172.24.24.93 port=9001

• The workstation being used in this example has an IP address of 172.24.24.93, the SBC is using an IP which is on the same subnet.

Launch Gstreamer on your workstation.

gst-launch-1.0 tcpserversrc host=0.0.0.0 port=9001 ! jpegdec ! videoconvert ! autovideosink sync=false

On the Gateworks SBC run:

python3 classify.py

Here's a video demonstration of the output you can expect if everything is working:

Video: ​https://youtu.be/pss6Gy_8UI4