AI on a Pi? Believe it!
TLDRThe video showcases a new Pineberry AI hat designed for the Raspberry Pi 5, utilizing the PCIe Express bus and an M2 slot to integrate the Coral AI Edge TPU. The setup aims to enhance AI capabilities on the Raspberry Pi, demonstrating its effectiveness through home surveillance with TPU-accelerated machine learning using frigate's open-source NVR. The video details the assembly, software setup, and performance testing of the hardware, highlighting the cost-effectiveness and potential of the TPU in AI applications. The creator also explores the possibility of using multiple TPUs and the upcoming official M2 hat from Raspberry Pi, emphasizing the potential for advanced AI functionalities and storage expansion.
Takeaways
- 🌟 The Pineberry AI hat is a new accessory designed to connect with Raspberry Pi 5 using the PCIe Express bus, introducing an M.2 slot for the Coral AI Edge TPU.
- 🚀 The Coral AI Edge TPU, costing $25, can outperform a $2,000 CPU in terms of AI capabilities.
- 🎥 The setup was tested using Frigate's open-source NVR home surveillance software, which utilizes TPU-accelerated machine learning for better performance.
- 💡 The Raspberry Pi, with the AI hat and TPU, can automatically start recording when a person enters the frame due to the machine learning model detecting a person.
- 🔧 The process of setting up the AI hat and TPU with Raspberry Pi 5 involves mounting the TPU, connecting the FPC ribbon, and securing the AI hat.
- 💰 The total cost for the setup, including the Raspberry Pi 5, AI hat, and Coral AI chip, is approximately $44 USD.
- 🛠 The PCIe version of the interface has thermal management features, allowing it to scale down power draw and inference speed to maintain acceptable operating temperatures for 24/7 use.
- 📋 The script provided in the video automates the process of setting up the Coral AI, downloading the necessary drivers, tweaking the operating system, and exposing the device.
- 📱 The test for the successful installation of the TPU is to print out the device information, confirming its visibility to the system.
- 🏠 Home surveillance with Frigate can be optimized by using a webcam or an IP camera, with the TPU enabling efficient person detection and event logging.
- 🔮 The Raspberry Pi 5 with the PCIe Express bus and Coral AI Edge TPU provides a smoother and efficient experience for running AI models and applications.
Q & A
What is the new component introduced in the Pineberry AI hat designed to connect to?
-The Pineberry AI hat is designed to connect to the Raspberry Pi 5 via the PCIe Express bus, which introduces an M.2 slot specifically engineered to fit the Coral AI Edge TPU.
What is the significance of the Coral AI Edge TPU in the setup described?
-The Coral AI Edge TPU is significant because it can outperform a $2,000 CPU in terms of AI processing capabilities. It brings AI acceleration to the Raspberry Pi setup, enabling faster inference times and efficient machine learning tasks.
How does the Raspberry Pi setup with the TPU enhance the performance of frigate's open source NVR home surveillance?
-By integrating the TPU with the Raspberry Pi, the setup leverages machine learning to accelerate the processing of video data for home surveillance. This results in faster detection and recording of events, improving the overall efficiency and effectiveness of the surveillance system.
What is the total cost of the Raspberry Pi 5 setup with the AI hat and TPU?
-The total cost of the setup is approximately $44 USD, with the AI hat priced at $19 and the Coral AI chip at $25.
How does the PCIe version of the TPU accelerator differ from the USB version?
-The PCIe version of the TPU accelerator is technically faster than the USB version and also includes thermal management features. This allows the system to dynamically scale down power draw and inference speed if the hardware gets too hot, ensuring it operates within acceptable ranges, which is beneficial for 24/7 operation.
What is the purpose of running the script provided in the script section of the transcript?
-The script is designed to automate the setup process for the Coral AI TPU on the Raspberry Pi. It downloads the necessary drivers, tweaks the operating system settings, and exposes the device, making it ready for use with Docker and other AI applications.
Why is the TPU device exposure crucial after its installation?
-Once the TPU device is exposed, it becomes visible to Docker and other AI applications like the Coral AI library and frigate. This is essential for leveraging the full capabilities of the TPU for tasks such as image classification and object detection.
What is the role of the Debian 10 Docker instance in the setup?
-The Debian 10 Docker instance is used to run the Coral AI library, which has been somewhat neglected and requires an older version of Python. By running it in a Docker container, the user can avoid potential compatibility issues with the host system and utilize the TPU for AI tasks.
How does the Raspberry Pi 5 perform with the TPU compared to other devices?
-The Raspberry Pi 5 performs very well with the TPU. The user noted that it runs smoother than on an x86 device, and the hardware and software integration is solid, resulting in faster inference times and efficient machine learning processing.
What are some potential improvements or additions to the Raspberry Pi setup mentioned in the transcript?
-The user suggests that it might be possible to use a dual Edge TPU for加倍资源, and also mentions the potential use of the Raspberry Pi's upcoming official M.2 hat. Additionally, the user considers using the USB accelerator to leave the PCIe slot open for fast NVMe storage.
Outlines
🤖 Introducing the Pineberry AI Hat and Coral AI Edge TPU
This paragraph introduces the Pineberry AI hat, a device designed to connect with the Raspberry Pi 5 through a PCIe Express bus. It highlights the inclusion of an M.2 slot specifically engineered to accommodate the Coral AI Edge TPU. The script emphasizes the superior performance of a $25 Coral device over a $2,000 CPU and explores the potential of using this interface to bring AI capabilities to the Raspberry Pi like never before. The video demonstrates running frigate's open-source NVR home surveillance with TPU-accelerated machine learning to assess its effectiveness and value. The setup includes a Raspberry Pi, an edge TPU, a $15 webcam, and control from a warp terminal. The paragraph also discusses the technical specifications, installation process, and cost of the setup, comparing the PCIe version to the USB accelerator and emphasizing the benefits of the PCIe version's thermal management features for 24/7 operation.
🛠️ Setting Up the Raspberry Pi with TPU and Docker
This section delves into the process of setting up the Raspberry Pi with the TPU and using Docker for the AI applications. It outlines the steps for mounting the TPU onto the AI hat, securing the connections, and connecting the AI hat to the Raspberry Pi 5. The paragraph discusses the costs associated with the setup and compares it to other devices. The script then guides the viewer through the installation of Raspberry Pi OS, Wi-Fi network setup, and SSH configurations. It simplifies the process with a preconfiguration script that automates the installation of the Coral AI driver and system tweaks. The paragraph also covers the process of testing the TPU installation and making the device visible to Docker and frigate. It discusses the challenges of using the Coral AI library with the latest Python versions and proposes a solution using a Docker Debian 10 VM for running the pyCoral library.
🎥 Testing AI Performance with frigate and Edge TPU
The final paragraph focuses on testing the AI performance using frigate for home surveillance and the Edge TPU. It describes the process of setting up a webcam for proof of concept, installing MQTT, and configuring frigate with a YAML file. The paragraph emphasizes the successful detection of the TPU by frigate and the optimization of the FFMPEG directive for better performance. The script then demonstrates the live feed from the camera and the event-triggered recording feature of frigate, which uses a quantized TensorFlow model to detect persons in the frame. It discusses the flexibility of the system with various configurations and the potential for using different machine learning models and object detections. The paragraph concludes with a reflection on the smooth performance of the Raspberry Pi 5, the potential for using dual Edge TPUs, and suggestions for future configurations, such as using the camera module 3 or installing Google's Coral library for custom model prototyping.
Mindmap
Keywords
💡Pineberry AI hat
💡Raspberry Pi 5
💡PCIe Express bus
💡Coral AI Edge TPU
💡frigate's open source NVR
💡M.2 slot
💡inference times
💡thermal management
💡Docker
💡mqtt
💡person detection
Highlights
The introduction of the Pineberry AI hat, a new accessory for the Raspberry Pi 5.
The Pineberry AI hat connects via the PCIe Express bus, featuring an M2 slot designed for the Coral AI Edge TPU.
A $25 Coral device can outperform a $2,000 CPU in terms of AI capabilities.
The setup uses frigate's open-source NVR home surveillance with TPU-accelerated machine learning.
The Raspberry Pi, with the Edge TPU, can start recording when a person enters the frame.
The Raspberry Pi was removed from the recommended hardware list, but the user achieved faster inference times than listed devices.
The TPU installation involves mounting it onto the AI hat and connecting it to the Raspberry Pi 5.
The total cost for the AI hat and Coral AI chip is approximately $44 USD.
The PCIe version has thermal management, dynamically adjusting power draw and inference speed for 24/7 operation.
A script is used to set up the Coral AI, downloading drivers and tweaking the operating system.
The TPU installation test involves printing out the Apex device.
Docker and frigate are used for home surveillance NVR, leveraging the Coral AI Edge TPU.
The use of an older version of Python is recommended for the Coral library.
A Docker Debian 10 VM is set up to run pyCoral inside it for AI model inference.
Frigate is set up with an IP camera or a generic webcam for AI-powered surveillance.
The TPU is found and working with frigate, as indicated by the logs showing 'TPU found'.
The system can handle person detection, recording when a person is in the frame.
The Raspberry Pi 5 runs smoothly with AI applications, outperforming x86 devices.
The possibility of using a dual Edge TPU on a Raspberry Pi for increased resources.
The Raspberry Pi's official M2 hat is expected, potentially supporting multiple Edge TPUs.
The USB accelerator's performance is compared to the PCIe version, with suggestions to use the USB for leaving the PCIe slot open for NVMe storage.
