Raspberry Pi AI Kit - Unboxing and Installation Guide

Hailo
6 Jun 202416:20

TLDRGilad introduces the new Raspberry Pi AI Kit with the Halo 8l AI accelerator, capable of 13 TOPS at 2W power consumption. The kit is available at official resellers and comes with a community platform and developer zone. It includes three basic pipelines for detection, pose estimation, and instant segmentation, all built in Python for easy integration. The video demonstrates the installation process, including setting up the Pi OS, configuring the PCIe to Gen 3 for optimal performance, and installing the necessary software components. It also showcases running the demos with different options and inputs, emphasizing the ease of use and community support.

Takeaways

  • 🤖 The Raspberry Pi AI Kit features the Halo 8l AI accelerator, capable of delivering 13 TOPS with a power consumption of around 2 Watts.
  • 🛒 The kit is available at official Raspberry Pi resellers and comes with a pre-installed thermal pad for cooling.
  • 🔗 The Halo Community platform and developer Zone are now open, with resources and support available through provided links.
  • 📱 The installation process is streamlined for ease of use, focusing on local data processing for privacy, performance, and cost management.
  • 💻 Examples provided are open source and built in Python for easy integration into projects.
  • 👀 Three basic pipelines are released for tasks such as detection, pose estimation, and instant segmentation.
  • 📚 The Raspberry Pi's official rpy cam apps repo has integrated Halo inference for the camera framework.
  • 🔧 A detailed installation guide is provided for setting up the Raspberry Pi with the AI Kit, including system updates and driver support.
  • 🛠️ The kit requires specific components like a Raspberry Pi 5, camera, display cable, and power supply, along with an active cooler.
  • 🔄 Post-installation, users can verify the setup using the Halo RT CLI tools to ensure the chip is correctly identified and functioning.
  • 🎥 Demo applications showcase the capabilities of the AI Kit, including real-time object detection and pose estimation at high frame rates.

Q & A

  • What is the new AI kit introduced by Gilad?

    -The new AI kit introduced by Gilad features the Halo 8l entry level AI accelerator, designed for Raspberry Pi.

  • What performance does the Halo 8l AI accelerator deliver?

    -The Halo 8l AI accelerator delivers 13 trillion operations per second (TOPS) with a typical power consumption of around 2 Watts.

  • Where can you find the Raspberry Pi AI kit?

    -The Raspberry Pi AI kit can be found at official Raspberry Pi resellers.

  • What is being launched alongside the AI kit?

    -Alongside the AI kit, the Halo Community platform and the developer Zone are being launched.

  • What are the three basic pipelines released for different tasks?

    -The three basic pipelines released are for tasks of detection, pose estimation, and instant segmentation.

  • How are the pipelines built for integration?

    -The pipelines are built in Python for easy integration.

  • What has Raspberry Pi integrated into its official rpy cam apps repo?

    -Raspberry Pi has integrated Halo inference into its official rpy cam apps repo, which is the Raspberry Pi C++ camera framework.

  • What is the recommended Raspberry Pi model for the AI kit?

    -The recommended model is the Raspberry Pi 5, specifically the 64-bit OS version.

  • What is the purpose of the thermal pad in the AI kit?

    -The thermal pad in the AI kit is pre-installed between the MD2 and the board to help with heat dissipation, reducing the need for additional heat sinks if the design is well ventilated.

  • How can users verify the successful installation of the Halo software?

    -Users can verify the successful installation of the Halo software by running the 'Halo RT CLI firmware control identify' command to ensure the chip is recognized.

  • What are the additional software components installed with the 'sudo apt install Halo-all' command?

    -The command installs Halo firmware, Halo RT runtime software, the Halo Tapas core package, and the rpy cam apps Halo postprocessing software stages.

  • How can users configure their environment for the basic pipelines?

    -Users can configure their environment by cloning the repo, changing to the repo directory, and sourcing the setup.sh script which sets up the virtual environment and dependencies using the Tapas score package.

  • What are the components of the application structure for the demos?

    -The application structure consists of a user-defined data class, an application callback function, and a Gstreamer replication class, which sets up the Gstreamer pipeline and handles event and callbacks.

  • How can users run the detection example with different network options?

    -Users can run the detection example with different networks by using the '--network' flag and selecting options such as 'yolov6n', 'yolov8s', or 'yolox_s'.

  • What are the additional options available for the detection application?

    -Additional options include controlling the input source, enabling additional postprocessing with the '--use-frame' flag, showing FPS with '--show-fps', disabling synchronization with '--disable-sync', and selecting different networks with the '--network' flag.

  • How can users run the pose estimation and instance segmentation examples?

    -Users can run the pose estimation example by executing the corresponding command line provided in the script. For instance segmentation, they can run the example using a video file by copying and pasting the provided command line.

  • What is the recommended method to find the correct device location for a USB camera?

    -The recommended method is to use the 'ffmpeg' function, which will list the available camera files, usually even numbers like video0, video2, video4, etc. Users can try each one until the correct device is found.

Outlines

00:00

🌟 Introduction to Raspberry Pi AI Kit with Halo AI Accelerator

Gilad introduces the new AI kit for Raspberry Pi, featuring the Halo 8l entry-level AI accelerator. The kit is capable of delivering 13 trillion operations per second (TOPS) with a power consumption of around 2 Watts. It is available at Raspberry Pi's official resellers and comes with a community platform and developer zone. The kit aims to simplify data processing, ensuring local privacy, optimized performance, and cost management. The examples provided are open-source, and the video will guide through the installation process and showcase available examples.

05:03

🛠️ Setting Up the Raspberry Pi for AI Kit Installation

This section details the prerequisites for setting up the Raspberry Pi AI kit, including the Raspberry Pi 5, the AI kit itself, a micro HDMI to HDMI adapter, an active cooler, a Raspberry Pi camera, and a power supply. Gilad demonstrates the unboxing and assembly process, emphasizing the inclusion of a thermal pad and the potential need for additional heat sinks. The video then moves on to the software setup, which includes updating the Raspberry Pi OS, accessing the GitHub repository for Halo AI examples, and beginning the installation process with the provided guides.

10:05

🔧 Optimizing Performance and Installing Halo Software

To achieve optimal performance, the video explains the necessity of setting the PCIe to Gen 3 for the Halo device. It guides viewers through the process of accessing the 'raspy config' UI and enabling Gen 3. The installation of the Halo software involves running a command that installs the firmware, runtime software, and the Halo core package, which is derived from the Tapas repository. The video also covers the installation of the rpy cam apps and the verification of the installation through specific commands. If issues arise, viewers are directed to the Halo Community forum for troubleshooting.

15:06

📹 Running AI Pipelines and Exploring Application Structure

The video script outlines the process of running AI pipelines for tasks such as detection, pose estimation, and instance segmentation using the Raspberry Pi. It explains how to set up the environment using the Tapas score package and how to install the necessary requirements. The script also reviews the application structure, which includes a user-defined data class for communication between the main application and the callback function, the callback function itself where frame processing occurs, and the game replication class that sets up the GStreamer pipeline. The video demonstrates running the detection example using different YOLO networks and shows how to run the application with various input sources, including video files and USB cameras.

🔄 Running Pose Estimation and Instance Segmentation with USB Cameras

This part of the script focuses on running the pose estimation and instance segmentation examples. It provides instructions on how to execute these applications and shows the output, such as detected persons and their coordinates. The video also covers how to run the applications with USB cameras, including identifying the correct device file for the camera and running the instant segmentation with the USB input. The script invites viewers to join the community, follow the channel for updates, and share their suggestions or ideas in the comments.

Mindmap

Keywords

💡Raspberry Pi

Raspberry Pi is a series of small single-board computers developed to promote the teaching of basic computer science in schools and in part for self-learning. In the video, the Raspberry Pi is the central hardware platform for the AI Kit, which is used for running AI applications with the help of the Halo 8L AI accelerator.

💡Halo 8L

The Halo 8L is an entry-level AI accelerator mentioned in the script, designed to work with the Raspberry Pi to enhance AI capabilities. It is capable of delivering 13 trillion operations per second (TOPS) with a power consumption of around 2 Watts, which is significant for AI tasks such as image recognition and processing.

💡AI Kit

The AI Kit is a package that includes hardware and software components for developing AI applications. In the context of the video, the AI Kit features the Halo 8L accelerator and is aimed at making AI development more accessible with the Raspberry Pi platform.

💡TOPS

TOPS stands for trillions of operations per second and is a measure of the computational performance of AI accelerators. In the script, the Halo 8L is said to deliver 13 TOPS, indicating its ability to perform a large number of AI-related calculations quickly.

💡Halo Community platform

The Halo Community platform is introduced as a place for users to engage with others interested in AI development using the Halo technology. It serves as a hub for sharing ideas, projects, and troubleshooting, which is essential for a collaborative and supportive developer community.

💡Data processing

Data processing refers to the manipulation and analysis of data. In the video, keeping data processing local is emphasized to ensure privacy, optimize performance, and manage costs according to user preferences, which is a critical aspect when dealing with sensitive or proprietary information.

💡Open source

Open source refers to a type of software where the source code is available to the public, allowing anyone to view, use, modify, and distribute it. In the script, all Halo examples are open source, encouraging users to incorporate them into their projects and contribute to the community.

💡Pipelines

In the context of the video, pipelines refer to pre-defined sequences of processing steps for specific AI tasks like detection, pose estimation, and instant segmentation. These pipelines are built in Python for easy integration and are part of the AI Kit's offerings.

💡Pose estimation

Pose estimation is an AI technique used to detect and locate the parts of a subject's body in an image or video. In the script, it is one of the three basic pipelines released with the AI Kit, allowing users to identify and track human poses using the Raspberry Pi and Halo 8L.

💡Instance segmentation

Instance segmentation is a process in computer vision that not only segments an image into different objects but also identifies each individual object. The script mentions an instant segmentation example, demonstrating the AI Kit's capability to perform this advanced task.

💡GStreamer

GStreamer is a framework that allows for the construction of a wide range of multimedia applications. In the video, the Halo core package is based on GStreamer, facilitating the development of applications with media handling capabilities, which is integral to processing video for AI tasks.

💡RPi Cam

RPi Cam refers to the official camera module for the Raspberry Pi, which can be used for capturing images and video. The script mentions the integration of Halo inference into the Raspberry Pi's official camera framework, indicating that the AI Kit is compatible with this camera for enhanced image processing.

💡YOLO

YOLO stands for 'You Only Look Once' and is a popular real-time object detection system in computer vision. The script mentions YOLO v6n, YOLO v8s, and YOLO XS as the default networks used in the detection application of the AI Kit, showcasing the versatility in network selection for different performance and accuracy needs.

Highlights

Introduction of the new Raspberry Pi AI Kit featuring the Halo 8l AI accelerator.

The AI kit delivers 13 TOPS with a power consumption of around 2 Watts.

Availability of the kit through official Raspberry Pi resellers.

Launch of the Halo Community platform and the opening of the developer Zone.

Efforts to ensure a straightforward installation process for data privacy and performance optimization.

All Halo examples are open source, encouraging their use in projects and products.

Release of three basic pipelines for detection, pose estimation, and instant segmentation.

Integration of Halo inference into the official Raspberry Pi camera framework.

Overview of the required components for the Raspberry Pi setup.

Instructions for unboxing and connecting the Raspberry Pi 5 and accessories.

Pre-installed thermal pad in the AI kit for efficient heat dissipation.

Steps to install the new Pi OS and access the GitHub repository for examples.

Detailed guide for updating the Raspberry Pi system and installing the Halo packages.

Enabling PCIe Gen 3 for optimal performance on the Halo device.

Installation of the Halo software components including firmware and runtime software.

Verification of the installation through specific commands to ensure device identification.

Instructions for setting up the environment and installing application requirements.

Explanation of the application structure for the basic pipelines.

Demonstration of the detection application using YOLO v6n and other supported networks.

Options to control input sources and additional postprocessing in the callback function.

Performance showcase of the pose estimation and instance segmentation applications.

Guidance on running applications with USB input and the use of FFplay for device detection.

Invitation to join the Halo community and stay updated for future projects and examples.

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