Real-Time AI on Raspberry Pi with Google Coral's TPU USB Accelerator

Ben
Ben
@benjislab

Introduction

Raspberry Pi, a credit-card sized computer, has become a cornerstone in the DIY tech world. Known for its affordability and flexibility, it provides a playground for enthusiasts and professionals alike to experiment, learn, and create innovative solutions. Whether it's setting up a home automation system, building a personal web server, or creating a retro gaming console, the possibilities with Raspberry Pi are endless. Its accessible GPIO pins make it a favorable choice for interfacing with various hardware components, creating a bridge between the digital and physical worlds.

In the realm of computational power, though, there's a frontier that remains slightly elusive for the Raspberry Pi - Machine Learning (ML) and Artificial Intelligence (AI). This is where Google Coral's TPU (Tensor Processing Unit) USB Accelerator comes into the picture. This device is a compact, plug-in USB module that is capable of carrying out machine learning inference tasks at lightning speed. The TPU USB Accelerator is designed to work with lightweight, low-power devices, making it a perfect companion for the Raspberry Pi.

The intersection of Raspberry Pi and the TPU USB Accelerator brings forth the concept of edge AI into the limelight. Edge AI refers to running AI algorithms locally on the hardware device, reducing the need for data to travel back and forth to the cloud, thus speeding up the process and ensuring data privacy. The TPU USB Accelerator empowers the Raspberry Pi, allowing it to perform AI tasks that were previously only possible on machines with powerful GPUs or cloud-based platforms. This integration opens a new horizon of applications and projects that can be developed right at the edge, enabling real-time processing and decision-making, which is crucial in many scenarios such as real-time anomaly detection, facial recognition, and many more.

By merging the computational prowess of Google Coral's TPU USB Accelerator with the versatility of Raspberry Pi, a new doorway to the vast and growing field of edge AI has been unlocked. This synergy not only enriches the scope of projects that can be undertaken but also underscores a significant stride towards making AI more accessible and tangible for the maker community.

Getting the Essentials Ready

Before diving into the setup and exploration of what the combination of Raspberry Pi and Google Coral's TPU USB Accelerator can achieve, it's essential to have all the necessary materials at hand. Here's a list of what you'll need:

Materials Needed:

  • Raspberry Pi (any model with USB ports):
    • The Raspberry Pi is the heart of this setup. Any recent model with USB ports should work well, though models with higher processing power like the Raspberry Pi 4 would provide a smoother experience.
  • Google Coral TPU USB Accelerator:
    • This is the powerhouse that will accelerate the machine learning tasks. It's a plug-and-play device, making it extremely user-friendly.
  • MicroSD card with Raspbian OS installed:
    • A MicroSD card of at least 16GB is recommended, with the Raspbian OS installed. Raspbian is the official OS for the Raspberry Pi, optimized for its hardware.
  • Power Supply:
    • A suitable power supply to ensure that the Raspberry Pi runs smoothly. A 5.1V/3A power supply is recommended for the Raspberry Pi 4.
  • Keyboard, Mouse, and Monitor:
    • These peripherals are necessary for interacting with the Raspberry Pi during the setup process. Once the setup is complete, you might be able to operate your Pi remotely, depending on your project requirements.

The aforementioned items form the basic toolkit to kickstart your journey into exploring the capabilities of edge AI on a Raspberry Pi with the aid of Google Coral's TPU USB Accelerator. In the following sections, we'll walk through the steps to set up the Raspberry Pi, install necessary software, and delve into running your first machine learning model on this compact yet powerful setup.

Setting Up the Raspberry Pi

Setting up the Raspberry Pi is a straightforward process, particularly with the Raspberry Pi Imager which simplifies the process of installing an operating system. Here’s a step-by-step guide to getting your Raspberry Pi up and running:

Step 1: Flashing Raspberry Pi OS onto the MicroSD Card

  • Download the latest version of Raspberry Pi OS from the official website.
  • Install and open the Raspberry Pi Imager on your computer.
  • Select the Raspberry Pi OS (formerly Raspbian) from the list of available OS options within the Raspberry Pi Imager.
  • Select the MicroSD card you wish to flash from the list of available drives.
  • Click on the “Write” button and wait for the Raspberry Pi Imager to flash the MicroSD card.
  • Once the process is complete, remove the MicroSD card from your computer and insert it into the Raspberry Pi.

Step 2: Initial Boot

  • Connect your Raspberry Pi to a monitor, keyboard, and mouse.
  • Power up the Raspberry Pi using the appropriate power supply.
  • Follow the on-screen instructions to complete the initial setup, which includes setting your language, time zone, and connecting to a Wi-Fi network.

Step 3: Enabling SSH (Optional)

  • If you plan on accessing your Raspberry Pi remotely, it's a good practice to enable SSH.
  • Open the terminal and run the following command:
sudo raspi-config
  • Navigate to Interfacing Options > SSH and enable it.

Step 4: Updating and Upgrading the System

  • It’s crucial to have the latest updates and upgrades installed to ensure optimal performance and security.
  • Open the terminal and run the following command to update the package list:
sudo apt-get update
  • Once the update is complete, upgrade the installed packages to their latest versions with the following command:
sudo apt-get upgrade

Now your Raspberry Pi is set up with the latest version of Raspberry Pi OS, and you are ready to move on to installing the necessary software and libraries for interacting with the Google Coral TPU USB Accelerator. Ensuring your system is updated and upgraded provides a solid foundation for the steps that follow.

Installing Necessary Software and Libraries

To utilize the Google Coral TPU USB Accelerator with your Raspberry Pi, you'll need to install the Edge TPU runtime along with other necessary software and libraries. The process is straightforward and can be accomplished by following the steps outlined below:

Step 1: Installing the Edge TPU Runtime

  • The Edge TPU runtime is crucial for interfacing with the USB Accelerator. Open a terminal window on your Raspberry Pi and execute the following commands to download and install the Edge TPU runtime:
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
sudo apt-get install libedgetpu1-std

Step 2: Installing Other Necessary Libraries and Software

  • You'll need some additional software and libraries to run machine learning models on your Raspberry Pi. Install these dependencies with the following command:
sudo apt-get install python3-pip python3-dev
  • Now, install the TensorFlow Lite library which will allow you to run TensorFlow models on your Raspberry Pi:
pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime

Step 3: Verifying the Installation

  • To ensure that everything has been installed correctly, you can verify the installation of the Edge TPU runtime by running the following command:
dpkg -l | grep edgetpu

  • If the Edge TPU runtime is installed correctly, you should see an entry for libedgetpu1-std. Troubleshooting Dependencies

  • In case you encounter any dependency issues or errors during the installation, it's advisable to resolve them by searching for solutions on forums or the official documentation.

  • Additionally, the Google Coral website provides a comprehensive guide and a troubleshooting section to help resolve common issues.

With the necessary software and libraries installed, your Raspberry Pi is now ready to interface with the Google Coral TPU USB Accelerator. In the upcoming sections, we’ll guide you through connecting the USB Accelerator to your Raspberry Pi and running your first machine learning model on this setup.

Connecting the Google Coral TPU USB Accelerator

Connecting the Google Coral TPU USB Accelerator to your Raspberry Pi is a straightforward process. Here's how you do it:

Step 1: Connecting the USB Accelerator

  • Ensure your Raspberry Pi is powered off.
  • Plug the Google Coral TPU USB Accelerator into an available USB port on your Raspberry Pi.
  • Power on your Raspberry Pi.

Step 2: Verifying the Connection

  • Once your Raspberry Pi is powered on, it's essential to verify that it recognizes the USB Accelerator.
  • Open a terminal window on your Raspberry Pi.
  • Run the following command to list all connected USB devices:
lsusb
  • Look for a device named Google, Inc. in the output. This entry indicates that your Raspberry Pi has successfully recognized the USB Accelerator.
Bus 001 Device 004: ID 18d1:9302 Google Inc.
  • If you don't see the Google, Inc. entry, ensure that the USB Accelerator is properly connected and try running the lsusb command again.

With the Google Coral TPU USB Accelerator connected and recognized by your Raspberry Pi, you're now ready to run machine learning models on this setup. In the following sections, we'll guide you through downloading and installing a pre-trained model, and running it using the TPU USB Accelerator to experience the enhanced processing capabilities it brings to your Raspberry Pi.

Running Your First Model

Now that the setup is complete, it's time to run your first machine learning model on the Raspberry Pi using the Google Coral TPU USB Accelerator. For simplicity, we'll use a pre-trained model.

Step 1: Downloading and Installing a Pre-trained Model

  • Google Coral provides a variety of pre-trained models which you can find on their official website.
  • For this tutorial, let's download and use the MobileNet V2 model optimized for the Edge TPU. Run the following commands in your Raspberry Pi terminal:
mkdir coral && cd coral
wget https://github.com/google-coral/edgetpu/raw/master/test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite

Step 2: Running the Model using the TPU USB Accelerator

  • Create a python script named run_model.py and open it with your preferred text editor.
  • Paste the following code into run_model.py:
import numpy as np
import tflite_runtime.interpreter as tflite
import cv2

interpreter = tflite.Interpreter(model_path='coral/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite',
                                  experimental_delegates=[tflite.load_delegate('libedgetpu.so.1')])
interpreter.allocate_tensors()

image = cv2.imread('bird.jpg')
image_resized = cv2.resize(image, (224, 224))

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

input_data = np.expand_dims(image_resized, axis=0)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])

print('Predicted:', output_data)
Save the file and run the script using the following command:
bash
Copy code
python3 run_model.py

Step 3: Understanding the Output

  • Once the script runs, it will output a prediction based on the input image bird.jpg.
  • The output will be an array of probabilities corresponding to different bird species (as this model is trained on a bird dataset). The index with the highest probability indicates the predicted species of bird in the input image.
  • You can refer to the labels file associated with the model to map the index to the actual species name.

This example demonstrates how effortlessly you can run machine learning models on the Raspberry Pi using the Google Coral TPU USB Accelerator. The enhanced processing capabilities allow for real-time inference, opening a myriad of possibilities for more complex and interactive projects.

Exploring Further

With a successful run of your first machine learning model on the Raspberry Pi using the Google Coral TPU USB Accelerator, the door to a myriad of possibilities is now wide open. In this section, we'll explore further by looking into other models to try, resources for training custom models, and potential projects and applications you could dive into.

Trying Other Models

  • Pre-trained Models: Google Coral provides a variety of pre-trained models apart from the one we used. These models are optimized for the Edge TPU and cover a range of tasks like image classification, object detection, and pose estimation. Explore the Google Coral Models page to find a model that intrigues you.
  • TensorFlow Hub: Another excellent resource is TensorFlow Hub, where you can find a multitude of pre-trained models for different tasks.
  • Converting Models: If you have a particular model in mind that's not optimized for the Edge TPU, you might be able to convert it using the Edge TPU Compiler.

Training Custom Models

  • Google Colab: For training custom models, Google Colab is a free resource that provides access to GPU/TPU resources. You can follow this guide on retraining an image classification model for the Edge TPU using Google Colab.
  • TensorFlow Lite: TensorFlow Lite provides documentation on how to train and convert models for mobile and embedded devices.
  • Edge TPU Tutorials: The Coral website has several tutorials on training models specifically for the Edge TPU.

Potential Projects and Applications

  • Real-time Object Detection: Implement a real-time object detection system that can identify and track objects in a video stream.
  • Smart Home Automation: Enhance your smart home setup by adding AI-driven features like face recognition for security or gesture control for home appliances.
  • Health Monitoring: Create a health monitoring system that can analyze medical imagery or monitor vital signs.
  • Robotics: Incorporate the TPU USB Accelerator in robotics projects to enable real-time decision-making based on environmental conditions.
  • Educational Projects: Use this setup as a learning tool to delve deeper into machine learning and AI, exploring how models are trained, refined, and deployed.

The journey into the realms of machine learning and edge AI with the Raspberry Pi and Google Coral TPU USB Accelerator is filled with learning, experimentation, and innovation. The resources and potential project ideas mentioned above are just the tip of the iceberg. With the rapid advancements in AI and ML technologies, the scope of what you can achieve with this compact yet powerful setup is bound to expand further.

Troubleshooting Common Issues

As with any tech project, you might encounter some hiccups along the way while setting up or running your Raspberry Pi with the Google Coral TPU USB Accelerator. In this section, we'll address some common issues and provide solutions to help you troubleshoot them.

  1. USB Accelerator Not Recognized
  • Problem: The Raspberry Pi doesn't recognize the USB Accelerator when running the lsusb command.
  • Solution: Ensure that the USB Accelerator is properly connected to the USB port. Try reconnecting it or using a different USB port. If the issue persists, reboot your Raspberry Pi.
  1. Error During Installation of Libraries
  • Problem: Errors encountered while installing the Edge TPU runtime or other necessary libraries.
  • Solution: Check your internet connection and ensure that your Raspberry Pi is updated (sudo apt-get update && sudo apt-get upgrade). If dependency errors arise, try installing the missing dependencies manually using apt-get or pip.
  1. Model Not Running or Giving Incorrect Outputs
  • Problem: The model doesn't run or gives incorrect or unexpected outputs.
  • Solution: Ensure that the model file is correctly specified in your script and that it's compatible with the Edge TPU. Verify that the input data is correctly formatted and matches the input requirements of the model.
  1. Performance Issues
  • Problem: The model runs slower than expected or experiences lag.
  • Solution: Verify that the Edge TPU runtime is correctly installed and that the model is optimized for the Edge TPU. Consider checking the system load using tools like htop to identify any performance bottlenecks.
  1. Inaccurate Predictions
  • Problem: The model provides inaccurate or inconsistent predictions.
  • Solution: Check the quality and format of the input data. If using a custom-trained model, consider revisiting the training data and process to ensure it's adequately trained.
  1. Connectivity Issues
  • Problem: Trouble connecting to the internet or other devices.
  • Solution: Check your network settings and ensure that all cables and connections are secure. Rebooting the Raspberry Pi and/or your router may resolve connectivity issues.

Conclusion

The journey through setting up and running a machine learning model on a Raspberry Pi with the Google Coral TPU USB Accelerator unveils a substantial augmentation in the capabilities of what one can achieve with a Raspberry Pi in the realm of AI and ML. The TPU USB Accelerator not only bridges the computational gap but opens up a vista of real-time processing and on-device inference which is pivotal in today’s growing edge AI landscape.

This setup empowers enthusiasts, makers, and professionals alike to delve into more complex projects, be it in home automation, real-time monitoring systems, robotics, or any domain where real-time decision-making and data processing are crucial. The compactness and the ease of setup make it an excellent platform for educational projects, allowing one to grasp the practical applications of AI and ML.

We've merely scratched the surface of what's possible. The world of AI is vast, and the combination of Raspberry Pi and Google Coral's TPU USB Accelerator is a robust yet flexible platform to explore it. The resources, pre-trained models, and community support further enrich the experience, providing a solid ground for anyone looking to venture into AI-driven projects.

We encourage you to experiment, explore different models, perhaps train your own, and see how the edge AI capabilities can be leveraged in your projects. The bounds of what’s possible are ever-expanding, and with tools like these at your disposal, you're well-equipped to push those boundaries further. Through exploration and experimentation, not only do you contribute to your personal growth, but you also contribute to a community that’s continually driving towards making technology more accessible and innovative.

So, plug in that TPU USB Accelerator, power up your Raspberry Pi, and step into a world where the potential for innovation is boundless. Your journey into the vast and exciting domain of AI has just begun, and there's much to discover and learn along the way.