AI on a Pi? Believe it!

Data Slayer
20 Jan 202412:28

TLDRThe video showcases the integration of the Pineberry AI hat with a Raspberry Pi 5, utilizing a PCIe Express bus and an M2 slot for the Coral AI Edge TPU. This setup promises superior AI performance at a fraction of the cost of traditional CPUs. The tutorial demonstrates setting up and running frigate, an open-source NVR home surveillance system, with TPU-accelerated machine learning for real-time person detection and recording. The process includes mounting the TPU, connecting it to the Raspberry Pi, and configuring the system with a script for optimal performance. The video also explores the potential of using multiple TPUs and the benefits of PCIe over USB for AI applications on the Raspberry Pi platform.

Takeaways

  • 🍓 The Pineberry AI hat is designed to connect to the Raspberry Pi 5 via the new PCIe Express bus, enhancing AI capabilities.
  • 🔗 It introduces an M2 slot specifically for the Coral AI Edge TPU, which can significantly outperform high-cost CPUs at a fraction of the price.
  • 🚀 The interface can be forced to use Gen 3 speeds, potentially bringing AI to the Raspberry Pi like never before.
  • 📹 A demonstration of using the setup with Frigate's open-source NVR home surveillance, showcasing TPU-accelerated machine learning in action.
  • 🛠️ The process includes mounting the TPU onto the AI hat, securing connections, and setting up the Raspberry Pi 5 with the necessary software.
  • 💻 The total cost for the setup is approximately $44 USD, with the AI hat at $19 and the Coral AI chip at $25, offering a cost-effective AI solution.
  • 🔥 The PCIe version has thermal management, dynamically adjusting power draw and inference speed to prevent overheating, ideal for 24/7 operation.
  • 📡 Setting up involves installing Raspberry Pi OS, configuring Wi-Fi, and enabling SSH for remote access, streamlining the process with a preconfiguration script.
  • 🔧 A script is provided to automate the setup, including downloading drivers, tweaking the OS, and exposing the TPU device.
  • 🔎 Testing the setup involves using the piee Coral library and Frigate for home surveillance, with the TPU found and operational in both tests.
  • 📈 The script and setup guide are detailed, providing a clear path for users to replicate the process and leverage AI on the Raspberry Pi.

Q & A

  • What is the new Pineberry AI hat designed to connect with?

    -The Pineberry AI hat is designed to connect with the Raspberry Pi 5 via the PCIe Express bus.

  • What is the purpose of the M2 slot in the Pineberry AI hat?

    -The M2 slot in the Pineberry AI hat is specifically engineered to fit the Coral AI Edge TPU.

  • How does the Coral device compare in performance to a high-end CPU?

    -A $25 Coral device can outperform a $2,000 CPU in terms of AI processing capabilities.

  • What is the advantage of using the PCIe version of the AI interface over the USB version?

    -The PCIe version can be forced to use Gen 3 speeds, potentially offering faster performance, and it includes thermal management to dynamically scale down power draw and inference speed when necessary.

  • What is the total cost for the setup mentioned in the script?

    -The total cost for the setup, including the AI hat and the Coral AI chip, is approximately $44 USD.

  • What is the role of the 16p FPC ribbon in the setup?

    -The 16p FPC ribbon is used to secure the connection between the AI hat and the Raspberry Pi 5.

  • What is the purpose of running the script mentioned in the script?

    -The script is used to download the necessary driver, tweak the operating system, and expose the Coral AI device to make it visible to Docker.

  • Why is it recommended to use an older version of Python with the Coral AI library?

    -The Coral AI library has been somewhat neglected, and it is recommended to use an older version of Python to ensure compatibility with the library.

  • What is the significance of the 'TPU found' entry in the frigate logs?

    -The 'TPU found' entry in the frigate logs indicates that the system was able to successfully detect and utilize the Google Coral hardware.

  • How does the person detection feature work in frigate?

    -The person detection feature in frigate uses a quantized TensorFlow model to analyze video frames. When a person is detected, it creates an event and starts recording until the person leaves the frame.

  • What is the potential benefit of using the camera module 3 with the Raspberry Pi 5 for AI applications?

    -The camera module 3, with its 12 MP sensor, could be used as an HD IoT camera, providing high-resolution imagery for AI applications.

Outlines

00:00

🤖 Introducing Pineberry AI Hat for Raspberry Pi 5

The video introduces a new Pineberry AI hat designed to connect with the Raspberry Pi 5 via a PCIe Express bus. This setup includes an M2 slot for the Coral AI Edge TPU, which is highlighted as a cost-effective solution for AI tasks, potentially outperforming more expensive CPUs. The video demonstrates the setup's capability by running an open-source NVR home surveillance system called frigate, which utilizes TPU-accelerated machine learning for object detection. The presenter shows the real-time recording feature triggered by the presence of a person, emphasizing the system's responsiveness and the potential of using such technology for home security.

05:02

🛠️ Setting Up the Raspberry Pi with Coral AI and frigate

The video continues with a step-by-step guide on assembling the Raspberry Pi with the AI hat and the Coral AI Edge TPU. It covers the process of securing the TPU to the AI hat, connecting it to the Raspberry Pi 5, and the costs involved, which total around $44 USD. The presenter also compares this setup with a USB accelerator, noting the advantages of the PCIe version, such as thermal management and potential for 24/7 operation. The video then proceeds to the software setup, including installing Raspberry Pi OS, configuring Wi-Fi and SSH, and running a script to automate the setup of the Coral AI device. The script is executed without root privileges, and upon completion, the system reboots, confirming the successful installation of the TPU.

10:05

🔍 Testing AI Capabilities with pieCoral and frigate

The video concludes with testing the AI capabilities of the setup. The presenter first attempts to use the pieCoral library, noting some challenges with outdated software and opting to run it within a Docker Debian 10 VM. After successfully running an inference test, the focus shifts to configuring frigate for home surveillance. The setup includes installing MQTT, a messaging relay service, and adjusting configuration files for optimal performance. The presenter demonstrates how to set up frigate with a webcam, despite the recommendation for IP cameras, as a proof of concept. The video shows the live feed and event logging features of frigate, highlighting the system's ability to detect and record events involving people. The presenter also discusses potential optimizations and the possibility of using multiple Edge TPUs on a single Raspberry Pi, suggesting that one TPU is sufficient for most home surveillance needs.

Mindmap

Keywords

💡Pineberry AI hat

The Pineberry AI hat is a hardware accessory designed to interface with the Raspberry Pi 5, specifically through the PCIe Express bus. In the context of the video, it is highlighted for its ability to connect with the Coral AI Edge TPU, which is a key component for accelerating AI tasks on the Raspberry Pi. The hat is part of the setup that enables AI capabilities on a device traditionally known for its versatility in various projects but not necessarily for high-performance AI computations.

💡Raspberry Pi 5

The Raspberry Pi 5 is the latest iteration of the Raspberry Pi series, a line of small single-board computers used for various electronic projects. The video emphasizes its compatibility with the Pineberry AI hat and the Coral AI Edge TPU, showcasing how it can be utilized for AI applications. The Raspberry Pi 5 is central to the video's theme of demonstrating AI capabilities on a cost-effective and widely accessible platform.

💡PCIe Express bus

The PCIe Express bus is a high-speed serial computer expansion bus standard used for computer hardware devices. In the video, it is mentioned as the connection method between the Pineberry AI hat and the Raspberry Pi 5, allowing for the integration of the Coral AI Edge TPU. The PCIe bus is crucial for the performance of AI tasks, as it provides the necessary bandwidth for data transfer between the Raspberry Pi and the AI accelerator.

💡Coral AI Edge TPU

The Coral AI Edge TPU is a hardware accelerator designed to speed up machine learning tasks, particularly those involving TensorFlow Lite models. The video discusses how this $25 device can outperform a much more expensive CPU in certain AI tasks. It is a central component in the video's demonstration of AI on a Raspberry Pi, showing its potential for making AI more accessible and affordable.

💡frigate

Frigate is an open-source NVR (Network Video Recorder) home surveillance system that uses machine learning for object detection. In the video, the setup with the Coral AI Edge TPU is tested by running frigate to see how well it can handle AI-accelerated surveillance tasks. The use of frigate exemplifies practical applications of AI on the Raspberry Pi, showcasing its potential for home automation and security.

💡USB 3.0

USB 3.0 is a third-generation USB technology that supports faster data transfer rates compared to its predecessors. The video contrasts the USB 3.0-based accelerator with the PCIe version, noting that the latter is technically faster and has thermal management features. USB 3.0 is relevant to the discussion as it represents an alternative method for connecting AI accelerators to the Raspberry Pi, though it may not offer the same performance as PCIe.

💡Docker

Docker is an open-source platform that enables developers to package applications and their dependencies into containers that can run consistently across different computing environments. In the video, Docker is used to create an environment where the Coral AI can be utilized, demonstrating how containerization can simplify the deployment of AI applications on the Raspberry Pi.

💡TensorFlow Lite

TensorFlow Lite is a lightweight solution for running machine learning models on mobile and embedded devices. The video mentions the use of TensorFlow Lite models in the context of the Coral AI Edge TPU, indicating how these models can be optimized for devices with limited resources. TensorFlow Lite is a key technology for enabling AI on devices like the Raspberry Pi.

💡MQTT

MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol for small sensors and mobile devices, ideal for situations where bandwidth is limited. In the video, MQTT is installed as part of setting up frigate, highlighting its role in the communication between devices in a home surveillance system. MQTT is essential for the efficient exchange of messages in IoT applications.

💡RTSP

RTSP (Real Time Streaming Protocol) is a network control protocol used for establishing and controlling media sessions between end points. The video refers to RTSP streams in the context of video surveillance, where it is used to control media streaming from cameras. RTSP is a critical component in video surveillance systems, enabling real-time video transmission and control.

💡NVMe

NVMe (Non-Volatile Memory Express) is a protocol designed to reduce latency to storage media attached to a computer, particularly solid-state drives. The video suggests using NVMe storage in conjunction with the Raspberry Pi for high-speed data access, indicating its potential for enhancing the performance of AI applications that require fast data read/write capabilities.

Highlights

Introduction of the Pineberry AI hat for Raspberry Pi 5

Connection to Raspberry Pi 5 via PCIe Express bus

M2 slot engineered to fit the Coral AI Edge TPU

Coral AI Edge TPU outperforms high-end CPUs at a fraction of the cost

Demonstration of TPU-accelerated machine learning for home surveillance

Raspberry Pi setup with $15 webcam and OLED screen

Real-time recording triggered by motion detection

Raspberry Pi removed from recommended hardware list but still achieves faster inference times

Mounting the TPU onto the AI hat and connecting to Raspberry Pi 5

Cost breakdown of the setup: AI hat for $19 and Coral AI chip for $25

PCIe version offers thermal management for 24/7 operation

Setting up Raspberry Pi OS 64bit and configuring Wi-Fi and SSH

Script to get Coral AI working by downloading driver and tweaking OS

Testing the TPU installation by printing out the Apex device

Using Docker to run Coral AI and frigate for home surveillance

Running pie Coral inside a Docker Debian 10 VM

Setting up frigate with MQTT and configuring the webcam

Optimizing frigate configuration for better performance

Real-time person detection and event logging with frigate

Potential of using the camera module 3 for HD IoT camera applications

Possibility of running dual Edge TPUs on a single Raspberry Pi

Recommendation to use the USB accelerator for flexibility

Discussion on the performance and potential of the setup

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