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, available through official Raspberry Pi resellers, includes an installation guide and open-source examples for tasks like detection, pose estimation, and instant segmentation. The video demonstrates the unboxing, setup, and running of the AI Kit, showcasing the ease of integration with the RPi and the community platform for support and collaboration.

Takeaways

  • 📦 The Raspberry Pi AI Kit features the Halo 8l AI accelerator, offering 13 TOPS performance with a power consumption of about 2 Watts.
  • 🛒 The kit is available at official Raspberry Pi resellers and comes with the Halo Community platform and developer Zone access.
  • 🔒 The kit emphasizes local data processing for privacy, performance optimization, and cost management.
  • 💻 All examples provided by Halo are open source, encouraging community use in projects and products.
  • 🔧 Three basic pipelines are released for tasks like detection, pose estimation, and instant segmentation, all built in Python for easy integration.
  • 📷 Raspberry Pi has integrated Halo inference into its official rpy cam apps repo, which is a C++ camera framework.
  • 🔩 The unboxing process includes setting up the Raspberry Pi 5, an active cooler, camera, and other necessary components.
  • 🛠️ The installation guide provides a step-by-step process for setting up the Raspberry Pi OS, updating the system, and installing the necessary software packages.
  • 🔄 The installation requires enabling PCI Gen 3 for optimal performance, which is a new feature in the Raspberry Pi configuration UI.
  • 🔍 After installation, verification steps are provided to ensure the chip is identified and all software components are correctly installed.
  • 🚀 The script also covers how to set up and run demo applications, detailing the structure and customization of the application code.
  • 🎥 The demo applications showcase the capabilities of the AI Kit, including real-time detection, pose estimation, and instance segmentation.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the introduction of the Raspberry Pi AI Kit featuring the Halo 8l entry level AI accelerator and its installation guide.

  • What are the performance specifications of the Halo 8l AI accelerator?

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

  • Where can the Raspberry Pi AI Kit be purchased?

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

  • What is the purpose of the Halo Community platform and the developer Zone?

    -The Halo Community platform and the developer Zone are launched to provide support and resources for users, with links provided in the description.

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

    -The three basic pipelines released are for object detection, pose estimation, and instance segmentation.

  • How is the Raspberry Pi AI Kit integrated with the Raspberry Pi camera framework?

    -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, which is assumed to be used throughout the installation guide.

  • What steps are involved in the initial setup of the Raspberry Pi for the AI Kit?

    -The initial setup involves updating the Raspberry Pi OS, setting the PCIe to Gen 3 for optimal performance, and installing the Halo software packages.

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

    -Users can verify the installation by running commands like 'Halo RT CLI firmware control identify' to ensure the chip is recognized and checking the installation of the Tapas Halo and the Halo element.

  • What is the structure of the application for the basic pipelines?

    -The application structure consists of a user-defined data class for communication, an application callback function for processing each frame, and a Gstreamer replication class for setting up the pipeline and handling events and callbacks.

  • How can users customize the application for their specific needs?

    -Users can customize the application by modifying the network parameters and the pipeline in the 'get pipeline string' function of the Gstreamer replication class.

Outlines

00:00

😀 Introduction to Raspberry Pi's 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, which offers 13 TOPS performance at around 2 Watts power consumption, is available through official Raspberry Pi resellers. The release is accompanied by the launch of the Halo Community platform and the opening of the developer zone. The focus is on easy installation, local data processing for privacy, performance optimization, and cost management. All examples provided are open source, and the community is encouraged to use them in their projects. Three basic pipelines for detection, pose estimation, and instant segmentation are released, all built in Python for easy integration. Raspberry Pi has also integrated Halo inference into its official rpy cam apps repo.

05:03

🛠️ Setting Up the Raspberry Pi with Halo AI Kit

The script details the components needed for the setup, including the Raspberry Pi 5, the Halo AI kit, a micro HDMI to HDMI adapter, an active cooler, a Raspberry Pi camera, and a power supply. It walks through the unboxing and initial setup process, including connecting the cooler and preparing the camera. The AI kit comes with a pre-installed thermal pad, potentially eliminating the need for additional heat sinks. The script then guides the user through the installation of Pi OS, accessing the GitHub repository for examples, and the installation process, which includes updating the Raspberry Pi, installing the Halo driver support, and setting the PCIe to Gen 3 for optimal performance.

10:05

🔧 Installation and Configuration of Halo Software on Raspberry Pi

The script explains the necessary steps to install the Halo software on the Raspberry Pi, which includes running a command to install various components like the Halo firmware, RT runtime software, and the Halo Tapas core package. It also covers the installation of the rpy cam apps and the verification of the installation through specific commands. The script provides instructions on how to configure the environment using the Tapas score package and how to set up the virtual environment for the demos. It also outlines the structure of the application, detailing the user-defined data class, the application callback function, and the game replication class, which are essential for running the basic pipelines.

15:06

📹 Running Demos and Exploring Advanced Options with the AI Kit

The script demonstrates how to run various applications using the AI kit, starting with a detection application that uses YOLO v6n as a default network but also supports other networks. It shows how to run the application with different flags to control input, enable additional postprocessing, and display FPS. The script also covers how to run pose estimation and instance segmentation examples, and it provides guidance on how to use a USB camera as an input source. The video concludes with an invitation to join the community, follow the channel for updates, and share suggestions or ideas for future projects.

Mindmap

Keywords

💡Raspberry Pi

Raspberry Pi is a series of small, low-cost, single-board computers developed in the UK by the Raspberry Pi Foundation to promote the teaching of basic computer science in schools and in developing countries. In the video, it is the main hardware platform for the AI Kit, which is used to demonstrate the installation and operation of the AI accelerator and various AI applications.

💡AI Kit

The AI Kit mentioned in the video refers to a bundle of hardware and software designed to facilitate the development and deployment of artificial intelligence applications. It features the Halo 8L AI accelerator, which is an integral part of the Raspberry Pi setup, enhancing its AI processing capabilities.

💡Halo 8L

Halo 8L is an entry-level AI accelerator that is part of the AI Kit. It is designed to deliver 13 trillion operations per second (TOPS) with a typical power consumption of around 2 Watts. It plays a central role in the video by providing the computational power needed for running AI models on the Raspberry Pi.

💡TOPS

TOPS stands for trillion operations per second and is a unit of measurement for the performance of AI accelerators. In the context of the video, the Halo 8L is capable of delivering 13 TOPS, indicating its efficiency in processing AI tasks on the Raspberry Pi.

💡Halo Community platform

The Halo Community platform is a resource introduced in the video for users to engage with the makers and developers community at Halo. It serves as a hub for sharing knowledge, getting support, and collaborating on projects that utilize the AI Kit and related technologies.

💡Developer Zone

The Developer Zone is an area or section opened to all users by Halo, likely to provide a space for developers to access tools, resources, and support for developing AI applications with the Raspberry Pi AI Kit. It is part of the community's efforts to encourage innovation and collaboration.

💡Data processing

Data processing in the video refers to the manipulation and analysis of data by the AI Kit. It is highlighted as being kept local, which means it occurs on the device itself, ensuring privacy, optimizing performance, and managing costs according to user preferences.

💡Open source

Open source refers to a type of software where the source code is made available to the public, allowing anyone to view, use, modify, and distribute the software for any purpose. In the video, all Halo examples are open source, encouraging users to incorporate them into their projects and products.

💡Pipelines

In the context of the video, pipelines refer to predefined sequences of processing steps for different AI tasks such as detection, pose estimation, and instance segmentation. They are built in Python for easy integration and are part of the AI Kit's offerings.

💡Pose estimation

Pose estimation is an AI task that involves determining the position and orientation of objects within an image or video, typically used for identifying human body parts and their arrangement. In the video, it is one of the three basic pipelines released for the AI Kit, demonstrating the capability of the Raspberry Pi with the Halo 8L to perform this task.

💡Instance segmentation

Instance segmentation is an advanced computer vision technique that not only segments different objects in an image but also identifies and distinguishes between instances of the same object class. The video showcases an instance segmentation example as part of the AI Kit's capabilities.

💡RPY CAM

RPY CAM refers to the Raspberry Pi Camera framework, which is a C++ library for accessing the camera module in Raspberry Pi devices. In the video, it is mentioned that Raspberry Pi has integrated Halo inference into its official RPY CAM apps repository, indicating a deeper integration of the AI Kit with the Raspberry Pi ecosystem.

Highlights

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

The AI accelerator 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 developer Zone.

Efforts to ensure a straightforward installation process for local data processing.

Open source examples provided for integration into projects and products.

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

Integration of Halo inference into the Raspberry Pi's official rpy cam apps repo.

Unboxing and setup of the Raspberry Pi 5 and the AA kit.

Instructions for connecting the active cooler and preparing the camera.

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

Installation guide for the Raspberry Pi with the Halo AI Kit.

Updating the Raspberry Pi OS and installing the latest Raspberry Pi Core with Halo driver support.

Optimizing performance by setting PCIe to Gen 3 through the raspi-config UI.

Installation of the Halo software components including firmware, runtime software, and Tapas core package.

Verification of the installation using the Halo RT CLI and Tapas Halo tools.

Demos of the detection, pose estimation, and instant segmentation applications.

Running the detection application with YOLO v6n and support for other YOLO networks.

Ability to run applications from file, USB camera, or Raspberry Pi camera.

Performance demonstration with the pose estimation and instant segmentation examples.

Guide on how to run applications with USB camera input.

Invitation to join the Halo community and follow for updates on new projects and examples.

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