AI on a Pi? Believe it!
TLDRThe video script introduces the Pineberry AI hat designed for the Raspberry Pi 5, utilizing the PCIe Express bus and M2 slot to integrate the Coral AI Edge TPU for enhanced machine learning performance. The setup, costing approximately $44, demonstrates the capabilities of the TPU in accelerating AI tasks, such as home surveillance with frigate's open-source NVR, showcasing the potential of this cost-effective and efficient hardware for AI applications.
Takeaways
- 🌟 The Pineberry AI hat is a new accessory designed to connect with the Raspberry Pi 5 using the PCIe Express bus, introducing an M2 slot for the Coral AI Edge TPU.
- 🚀 The $25 Coral device can outperform a $2,000 CPU, indicating a high cost-performance ratio for AI capabilities.
- 🎥 The setup was tested using frigate's open-source NVR home surveillance with TPU-accelerated machine learning to evaluate its effectiveness.
- 📈 The Raspberry Pi, with the Edge TPU, demonstrated faster inference times compared to other recommended hardware, showing its potential for AI applications.
- 💰 The total cost for the AI hat and Coral AI chip is approximately $44 USD, offering an affordable solution for AI integration.
- 🔧 The PCIe version of the interface has thermal management features, allowing for 24/7 operation without overheating.
- 📋 The script used for setup simplifies the process of getting the Coral AI working by downloading the driver and tweaking the operating system.
- 🏗️ The TPU installation is verified by printing out the Apex device, making it visible to Docker and frigate for AI applications.
- 📷 The use of the Coral AI chip was demonstrated with image classification using the Google Coral AI Python library.
- 👀 Frigate was set up to work with a webcam for home surveillance, utilizing the TPU for person detection and event logging.
- 🔍 The system's performance and resource allocation were monitored, showing promising results with a 7.49 millisecond inference time.
Q & A
What is the new component introduced in the Pineberry AI hat for the Raspberry Pi 5?
-The Pineberry AI hat introduces a PCIe Express bus with an M.2 slot specifically engineered to fit the Coral AI Edge TPU.
How does the Coral AI Edge TPU perform in comparison to a high-end CPU?
-A $25 Coral device can outperform a $2,000 CPU, making it a cost-effective solution for AI applications.
What is the significance of the TPU-accelerated machine learning in the context of home surveillance?
-The TPU-accelerated machine learning enhances the capabilities of the open-source NVR home surveillance system, Frigate, by enabling faster and more efficient object detection and recording.
What are the main components of the setup described in the script?
-The main components include a Raspberry Pi, the Pineberry AI hat with the Coral AI Edge TPU, a $15 webcam, an OLED screen, and the warp terminal for control.
How much does the total setup cost, and what are the individual costs of the AI hat and the Coral AI chip?
-The total setup costs around $44 USD, with the AI hat priced at $19 and the Coral AI chip at $25.
What is the advantage of the PCIe version over the USB version of the accelerator?
-The PCIe version has thermal management, allowing it to dynamically scale down power draw and inference speed when it gets too hot, making it suitable for 24/7 operation. It is also technically faster than USB 3.0.
How is the Coral AI device exposed to Docker and made visible for use?
-A script is run that downloads the necessary drivers, tweaks the operating system, and exposes the device, making it accessible for Docker and other applications.
What is the recommended version of Python for using the Google Coral AI Python library?
-The library recommends using an older version of Python, which can be managed using Anaconda or Mamba.
How is the home surveillance setup tested?
-The setup is tested using two main methods: leveraging the Google Coral AI Python library for image classification and using the home surveillance NVR system, Frigate.
What is the significance of the 'TPU found' entry in the logs?
-The 'TPU found' entry indicates that the system has successfully detected the Google Coral hardware, confirming that it is functioning correctly.
What additional hardware component is suggested for the Raspberry Pi setup?
-The script suggests the potential addition of a second Edge TPU for doubled resources, although a single TPU can support around 10 cameras, making it sufficient for most use cases.
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 performance benefits of using a $25 Coral device over a more expensive CPU, suggesting that it can bring AI capabilities to the Raspberry Pi like never before. The script then describes a test setup involving the use of frigate's open-source NVR home surveillance software, accelerated by machine learning through the TPU, to evaluate its effectiveness and value. The paragraph also details the process of mounting the TPU onto the AI hat, connecting it to the Raspberry Pi, and the costs associated with the setup. It mentions the thermal management features of the PCIe version of the device and its benefits for 24/7 operation.
🛠️ Setting Up the Raspberry Pi with TPU and Docker
The second paragraph delves into the setup process of the Raspberry Pi with the TPU installed. It outlines the steps to mount the TPU onto the AI hat, secure the connections, and connect the AI hat to the Raspberry Pi 5. The paragraph discusses the costs of the components and compares the performance of the PCIe version to the USB accelerator. The script then guides through the installation of Raspberry Pi OS, Wi-Fi network setup, and SSH configurations. It also covers the execution of a script to download drivers and tweak the operating system for the Coral AI device. The paragraph further explains the process of setting up a Debian 10 Docker instance to run the Coral AI library and the steps to run inferences using the Edge TPU. The paragraph concludes with the installation and configuration of mqtt for the frigate home surveillance system and the process of setting up the frigate with a webcam.
🎥 Testing AI Capabilities with frigate and Edge TPU
This paragraph focuses on testing the AI capabilities of the setup using frigate for home surveillance and the Coral AI library. It describes the process of running the classify image test using the Edge TPU and the results obtained from the inference. The script then moves on to set up frigate with a webcam, detailing the installation of mqtt and the configuration of the frigate system. It emphasizes the detection of persons in the frame and the automatic recording of events when a person is detected. The paragraph also discusses the optimization of the FFMPEG directive for better performance and the live feed capabilities of the system. Finally, it explores the potential of using multiple cameras, TPUs, and different machine learning models for more complex setups. The script concludes with a discussion on the potential of the Raspberry Pi 5 for AI applications, the possibility of using a dual Edge TPU, and the upcoming official M2 hat from Raspberry Pi. It also suggests considering the use of the USB accelerator to leave the PCIe slot open for NVMe storage to enhance the Raspberry Pi's performance.
Mindmap
Keywords
💡Pineberry AI hat
💡Raspberry Pi 5
💡PCIe Express bus
💡Coral AI Edge TPU
💡Frigate's open source NVR
💡Machine Learning
💡Inference
💡Thermal Management
💡Docker
💡mqtt
💡Performance Optimization
Highlights
The introduction of the Pineberry AI hat, a new accessory designed to connect with the Raspberry Pi 5.
The utilization of the PCIe Express bus, which includes an M.2 slot specifically engineered to fit the Coral AI Edge TPU.
The significant performance advantage of the $25 Coral device over a $2,000 CPU, indicating a shift in cost-effective AI capabilities.
The demonstration of running frigate's open source NVR home surveillance with TPU-accelerated machine learning to assess its effectiveness.
The setup of the Raspberry Pi with the Edge TPU and a $15 webcam, showcasing an affordable yet powerful AI surveillance system.
The removal of the Raspberry Pi from the recommended hardware list, yet still achieving faster inference times than listed devices.
The detailed process of mounting the TPU onto the AI hat and connecting it to the Raspberry Pi 5, emphasizing the ease of assembly.
The cost-effectiveness of the setup, with the AI hat at $19 and the Coral AI chip at $25, compared to a $60 USB accelerator.
The advantage of the PCIe version over the USB version, including thermal management for continuous operation.
The streamlined setup process for Raspberry Pi OS, Wi-Fi network, and SSH settings, highlighting the user-friendly nature of the system.
The creation of a single script to simplify the process of getting the Coral AI working, making it accessible to users.
The successful installation and testing of the TPU, demonstrated by the Apex device printout.
The use of Docker and the exposure of the TPU device, allowing for integration with various applications such as frigate.
The testing of the TPU with piee Coral and frigate, showing its versatility in both Google Coral AI python library and home surveillance NVR.
The optimization of the frigate configuration file for better performance, specifically the FFMPEG directive for hardware acceleration.
The successful implementation of person detection with frigate, which initiates recording whenever a person is detected in the frame.