Getting Started with Google Coral Development Board: Setup Guide

Table of Contents:

1. Introduction
2. Overview of the Google Coral development board
3. Specifications of the Google Coral development board
4. Setting up the Google Coral development board 4.1. Flashing Mendel Linux to a micro SD card 4.2. Installing Mendel Linux on the Coral Dev board 4.3. Booting up the Coral board
5. Connecting to the Coral board 5.1. Using an OTG cable 5.2. Connecting to a Wi-Fi network
6. Running demos on the Google Coral development board 6.1. Running object recognition demos 6.2. Running tracking demos 6.3. Running classification demos
7. Using the Google Coral board without a monitor
8. Additional examples and resources
9. Conclusion
10. FAQ

Article:

Introduction

Hey everyone, I recently got my hands on the Google Coral development board, and I must say, it's quite amazing! In this article, I'll give You a detailed overview of the Google Coral board and guide you through the setup process. We'll also explore the various demos and examples that Google has provided to help you make the most of this powerful development board. So, let's dive right in!

Overview of the Google Coral development board

The Google Coral development board is a compact and powerful device designed for edge computing applications. It features a four-Core ARM CPU made by NXP, along with 8 gigabytes of eMMC memory and 4 gigabytes of low-power DDR4 memory. The board also includes a micro SD slot, HDMI connector, 3.5 millimeter audio jack, proprietary video camera connector, gigabit Ethernet connector, and a Wi-Fi module. With a size similar to that of a Raspberry Pi 4 board, the Google Coral board offers impressive capabilities.

Specifications of the Google Coral development board

Let's take a closer look at the specifications of the Google Coral development board. The board is equipped with a powerful four-core ARM CPU, providing ample processing power for edge AI applications. It also includes 8 gigabytes of eMMC memory, allowing for fast and efficient data storage. Additionally, the board features a micro SD slot for expandable storage and 4 gigabytes of low-power DDR4 memory. With HDMI and audio connectors, as well as a proprietary video camera connector, the Google Coral board offers versatility in terms of connectivity options. It also includes a gigabit Ethernet connector and a Wi-Fi module for seamless network connectivity.

Setting up the Google Coral development board

Now that we have a good understanding of the Google Coral development board, let's proceed with the setup process. To get started, we need to flash Mendel Linux to a micro SD card and install it on the Coral Dev board. Here's a step-by-step guide to help you through the process.

4.1 Flashing Mendel Linux to a micro SD card

To begin, you'll need a computer and a micro SD card with a minimum capacity of 8 gigabytes. Make sure to backup any existing data on the micro SD card, as it will be wiped during the flashing process. Additionally, you'll need a 5-volt USB-C power supply and a USB-C data cable.

1. Download the Mendel Linux image from the link provided in the documentation prepared by Google.
2. Install the balenaEtcher software tool on your computer if you haven't already. This tool will be used to burn the Mendel image onto the micro SD card.
3. Connect the micro SD card reader to an available USB port on your computer.
4. Open balenaEtcher and choose the option to flash from file. Select the Mendel Linux image file you downloaded earlier.
5. Choose your micro SD card reader's drive letter from the available options in balenaEtcher (e.g., letter E).
6. Click the "Flash" button to start the flashing process. This may take around 5 to 10 minutes, depending on the speed of your adapter and micro SD card.
7. Once the flashing is complete, remove the micro SD card reader from your computer and insert the micro SD card into the Coral development board.

4.2 Installing Mendel Linux on the Coral Dev board

With the micro SD card containing the Mendel Linux image inserted into the Coral development board, it's time to install the operating system.

1. Set the micro switches on the Coral board to boot from the micro SD card. Use a toothpick or similar object to flip the switches to the "ON-OFF-ON-ON" position starting from the radiator side.
2. Connect the power supply to the Coral board, but do not connect the OTG cable to another computer at this point. Wait for the Mendel Linux installation to complete.
3. Google recommends waiting approximately 5 to 10 minutes for the installation to finish. It's best to wait at least 10 minutes to ensure the installation is successful.
4. Once the installation is complete, the board will power itself off, indicated by the red LED turning off.
5. Remove the power cable and the micro SD card from the board. Use a toothpick to change the position of the micro switches back to their original positions ("ON-OFF-OFF").
6. If you have a monitor connected to the Coral board, you should see the Mendel Linux interface. If not, don't worry - we'll cover how to use the Coral board without a monitor later in the article.

4.3 Booting up the Coral board

To boot up the Coral board and start using Mendel Linux, follow these steps:

1. Reconnect the Coral board to power, this time without connecting the OTG cable to another computer.
2. Wait for the board to boot up. This process typically takes around three minutes.
3. Once the boot process is complete, you should see the Mendel Linux interface on your monitor if you have one connected.
4. Congratulations! You have successfully set up the Google Coral development board and installed Mendel Linux.

Stay tuned for the next section, where we'll explore how to connect to the Coral board and start running demos.

Connecting to the Coral board

To Interact with the Google Coral board and run demos, we need to establish a connection. There are two methods for connecting to the board: using an OTG cable or connecting via a Wi-Fi network. Let's go through both methods.

5.1 Using an OTG cable

To connect to the Coral board using an OTG cable, follow these steps:

1. Connect one end of the OTG cable to the Coral board and the other end to your Ubuntu machine (or any other computer you're using).
2. On your host computer, ensure that the required tools for communicating with the board via OTG are installed. You can use the mdt devices command to check if your Coral board is listed.
3. Issue the mdt shell command to establish a secure connection between your devices.
4. Now, let's set up a Wi-Fi connection for the Coral board. Use the nmcli command to configure the Wi-Fi connection. The Coral board can detect 5 GHz Wi-Fi networks.
5. Voila! You are now connected to a wireless network on the Coral board.

5.2 Connecting to a Wi-Fi network

If you prefer to connect to the Coral board via a Wi-Fi network, follow these steps:

1. Open the network settings on the Coral board, either from the graphical interface or via the command line.
2. Choose your desired Wi-Fi network from the list of available networks.
3. Enter the password for the selected network, if prompted.
4. The Coral board will connect to the Wi-Fi network, and you will have an internet connection.

Congratulations! You are now connected to the Coral board and ready to start exploring its capabilities.

Stay tuned for the next section, where we will dive into running demos on the Google Coral development board.

Running demos on the Google Coral development board

The Google Coral development board comes with a plethora of pre-built demos and examples to help you get started with edge AI applications. In this section, we will walk through the process of running a few demos to showcase the power and versatility of the Coral board.

6.1 Running object recognition demos

One of the key capabilities of the Coral board is its ability to perform object recognition in real-time. Google has provided a variety of object recognition demos that you can easily run on the Coral board. Here's how to get started:

1. Download the object recognition demo code from the Google Coral GitHub repository.
2. Open the demo script in your preferred development environment.
3. Follow the instructions provided in the demo code to run the object recognition demo.
4. The Coral board will use the Edge TPU (Tensor Processing Unit) to perform object inference, providing fast and accurate results.

6.2 Running tracking demos

In addition to object recognition, the Google Coral board also supports object tracking. This allows you to track the movement of objects in real-time. To run a tracking demo on the Coral board, follow these steps:

1. Download the tracking demo code from the Google Coral GitHub repository.
2. Open the demo script in your preferred development environment.
3. Follow the instructions provided in the demo code to run the tracking demo.
4. The Coral board will utilize the Edge TPU to track objects, providing smooth and precise tracking results.

6.3 Running classification demos

Another exciting feature of the Google Coral board is its ability to perform image classification. You can train the board to recognize various objects and classify images accordingly. To run a classification demo on the Coral board, follow these steps:

1. Download the classification demo code from the Google Coral GitHub repository.
2. Open the demo script in your preferred development environment.
3. Follow the instructions provided in the demo code to run the classification demo.
4. The Coral board will use its powerful CPU and Edge TPU to classify images with high accuracy and speed.

You now have a good understanding of how to run demos on the Google Coral development board. In the next section, we'll explore how to use the board without a monitor and access it from any device and browser.

Using the Google Coral board without a monitor

If you don't have a monitor available, fret not! You can still use the Google Coral board without a monitor and access it from any device and browser. Here's what you need to do:

1. Ensure that the Coral board is connected to the same Wi-Fi network as the device you want to use to access it.
2. Open a browser on your device and enter the following address: 192.168.100.2:4664.
3. You should now see the Coral board's interface and be able to interact with it.
4. Switch between the Edge TPU and CPU inference models by pressing the end button.
5. Google has provided an abundance of examples and resources for you to explore.

Additional examples and resources

There is much more to explore with the Google Coral development board. Google has prepared a wide range of examples and resources to help you make the most of this powerful device. You can find these examples on the Google Coral GitHub repository. From object recognition and tracking to classification and much more, the possibilities are endless.

Conclusion

In conclusion, the Google Coral development board is a remarkable device for edge AI applications. Its powerful CPU and Edge TPU offer fast and efficient processing capabilities, while its compact size and versatile connectivity options make it easy to integrate into various projects. Whether you're a hobbyist or a professional developer, the Google Coral board opens up a world of possibilities in the field of edge computing. Don't hesitate to get your hands on one and start exploring the amazing potential of AI at the edge.

FAQ

Q: Can I use the Google Coral board with any operating system? A: Yes, you can choose an operating system of your choice to connect to your Coral board. The setup process may vary slightly depending on the OS you're using, but the overall functionality remains the same.

Q: Are there any limitations to the Wi-Fi connectivity of the Coral board? A: The Coral board can detect 5 GHz Wi-Fi networks, providing reliable and fast wireless connectivity. However, it's always a good idea to ensure that your Wi-Fi network meets the requirements for optimal performance.

Q: Can I run TensorFlow Lite models on the Google Coral board? A: Yes, the Google Coral board supports TensorFlow Lite models and provides the necessary APIs for running inference on the Edge TPU. You can take AdVantage of the powerful hardware acceleration offered by the Edge TPU to achieve fast and accurate results.

Q: What programming languages can I use with the Google Coral board? A: The Google Coral board supports various programming languages, including Python and C++. Google offers extensive documentation and resources for developers to help them get started with their preferred language.

Q: Can I use the Google Coral board for real-time video processing? A: Yes, the Google Coral board is capable of real-time video processing, thanks to its powerful hardware and efficient Edge TPU. You can use it for applications such as object tracking, live video analysis, and more.

Q: Is the Google Coral board suitable for beginners in AI and machine learning? A: While the Google Coral board offers advanced capabilities, beginners can still get started with it. Google provides comprehensive documentation and tutorials to guide users through the setup process and help them make the most of the board's features.

Q: Can I power off the Coral board remotely? A: Yes, you can remotely power off the Coral board by sending the sudo shutdown now command. This is similar to the process employed in Raspberry Pi devices.

Q: Does the Google Coral board support custom model training? A: Yes, the Google Coral board allows for custom model training using TensorFlow or other popular machine learning frameworks. You can train your own models on a separate machine and then deploy them on the Coral board for inference.

Q: Is the Google Coral board compatible with other Coral products? A: Yes, the Google Coral board is compatible with other Coral products, such as the Coral USB Accelerator. You can easily combine multiple Coral devices to enhance the capabilities of your AI applications.

Q: Can I use the Google Coral board for industrial applications? A: Yes, the Google Coral board is suitable for a wide range of industrial applications. Its compact size, low power consumption, and powerful hardware make it an ideal choice for edge computing in industrial environments.