Install M.2 Coral to Raspberry Pi 5
Introduction
The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module that brings two Edge TPU coprocessors to existing systems and products with an available M.2 E-key slot.
Raspberry Pi Fifth Flagship Development Computer is assembled with a powerful 2.4GHz 64-bit quad-core Arm processor and an 800MHz VideoCore VII GPU for impressive graphics. It offers advanced camera support, versatile connectivity, and enhanced peripherals, perfect for multimedia, gaming, and industrial tasks.
This wiki we will show you how to install Coral M.2 Accelerator to Raspberry Pi 5 and finally we will test Coral M.2 Accelerator.
Prepare Hardware
| Raspberry Pi 5 8GB | Raspberry Pi M.2 HAT+ | Coral M.2 Accelerator B+M key |
|---|---|---|
 |  |  |
Install Hardware
Install Python3.8
Coral software only support Python3.6-Python3.9, but the latest version of Raspberry Pi OS Python is Python3.11. So we need to install Python3.8.
Step 1: Update system
Open a terminal and run the following commands to update the system:
sudo apt update
sudo apt full-upgrade
Step 2: Install requirements
Open a terminal and run the following commands to install the required packages:
sudo apt-get install -y build-essential tk-dev libncurses5-dev libncursesw5-dev libreadline6-dev libdb5.3-dev libgdbm-dev libsqlite3-dev libssl-dev libbz2-dev libexpat1-dev liblzma-dev zlib1g-dev libffi-dev tar wget vim
Step 3: Download Python3.8
Open a terminal and run the following commands to download Python3.8:
wget https://www.python.org/ftp/python/3.8.0/Python-3.8.0.tgz
Step 4: Install Python3.8
Open a terminal and run the following commands to install Python3.8:
sudo tar zxf Python-3.8.0.tgz
cd Python-3.8.0
sudo ./configure --enable-optimizations
sudo make -j 4
sudo make altinstall
cd ..
Step 5: Check Python3.8
Open a terminal and run the following commands to check Python3.8:
python3.8 -V
The result should be:
Step 6: Create a virtual environment with Python3.8
python3.8 -m venv coral_venv
Configure Hardware Settings
Open a terminal and run the following commands to open config.txt:
sudo nano /boot/firmware/config.txt
And then add the following text to config.txt:
[all]
# Enable the PCIe External connector.
dtparam=pciex1
kernel=kernel8.img
# Enable Pineboards Hat Ai
dtoverlay=pineboards-hat-ai
Save and close the file by pressing CTRL+X, then Y to confirm. And then reboot the system.
sudo reboot
Check the kernel:
Open a terminal and run the following commands to check the kernel:
Make sure your kernel version 6.6.30 or higher
uname -a
Install the PCIe Driver and Edge TPU Runtime
Step 1: Enter the virtual environment
source coral_venv/bin/activate
Step 2: Install Edge TPU Runtime
Add the Google Coral Edge TPU package repository
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-get update
Install necessary packages and Edge TPU Runtime
sudo apt-get install cmake libedgetpu1-std devscripts debhelper dkms dh-dkms
Step 3: Install the PCIe driver
git clone https://github.com/google/gasket-driver.git
cd gasket-driver
sudo debuild -us -uc -tc -b
cd ..
sudo dpkg -i gasket-dkms_1.0-18_all.deb
Set Up the udev Rule Add a udev rule to manage device permissions:
open a terminal and run the following commands to set up the udev rule:
sudo sh -c "echo 'SUBSYSTEM==\"apex\", MODE=\"0660\", GROUP=\"apex\"' >> /etc/udev/rules.d/65-apex.rules"
sudo groupadd apex
sudo adduser $USER apex
sudo reboot
Check Edge TPU
lspci -nn | grep 089a
The result should be:
ls /dev/apex_0
Install the PyCoral library and test the Edge TPU
Step 1: Install the PyCoral library
source coral_venv/bin/activate
pip install --upgrade pip
python3 -m pip install --extra-index-url https://google-coral.github.io/py-repo/ pycoral~=2.0
Step 2: Test the Edge TPU
Install resources for the example:
mkdir coral && cd coral
git clone https://github.com/google-coral/pycoral.git
cd pycoral
bash examples/install_requirements.sh classify_image.py
Test the Edge TPU:
python3 examples/classify_image.py \
--model test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
--labels test_data/inat_bird_labels.txt \
--input test_data/parrot.jpg
The result should be:
Result
We have successfully installed the M.2 Coral accelerator on a Raspberry Pi 5 and tested the Edge TPU. We also ran the YOLOv8s model on the Coral M.2 Accelerator with int8 quantization, using an input size of 640x640 and a batch size of 1. The inference time is approximately 800-1000ms, which translates to around 1.1 frames per second (FPS).
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