Raspberry Pi AI Kit available now at $70

If you’ve ever wanted to experiment with the world of neural networks, artificial intelligence and machine learning on your Raspberry Pi 5, we have the perfect product for you: the Raspberry Pi AI Kit. Developed in collaboration with Hailo, the AI Kit offers an accessible way to integrate local, high-performance, power-efficient inferencing into a wide variety of applications. It’s available today from our network of Raspberry Pi Approved Resellers, priced at just $70.

The image shows two components commonly used in conjunction with Raspberry Pi devices. The larger component on the top is a Raspberry Pi M.2 HAT+ (Hardware Attached on Top) board. It features an M.2 connector for PCI Express devices and has mounting holes labeled for different M.2 sizes (2230 and 2242). There is also a ribbon cable connector and other electronic components on the board. The smaller component at the bottom is an M.2 module, likely an NVMe SSD or a different type of PCIe device. It has a metallic shield over the main chip and a gold connector edge designed to fit into the M.2 slot on the HAT+ board. The component also shows a few smaller electronic components and traces on its PCB (Printed Circuit Board). These components are used together to expand the functionality of a Raspberry Pi by adding high-speed storage or other peripherals through the M.2 interface.
The Raspberry Pi AI Kit disassembled

The Raspberry Pi AI Kit comprises our M.2 HAT+ preassembled with a Hailo-8L AI accelerator module. Installed on a Raspberry Pi 5, the AI Kit allows you to rapidly build complex AI vision applications, running in real time, with low latency and low power requirements. State-of-the-art neural networks for object detection, semantic and instance segmentation, pose estimation, and facial landmarking (to name just a few) run entirely on the Hailo-8L co-processor, leaving the Raspberry Pi 5 CPU free to perform other tasks.

Key features of the Raspberry Pi AI Kit include:

  • 13 tera-operations per second (TOPS) of inferencing performance
  • Single-lane PCIe 3.0 connection running at 8Gbps
  • Full integration with the Raspberry Pi image software subsystem
  • Compatibility with first-party or third-party cameras
  • Efficient scheduling of the accelerator hardware: run multiple neural networks on a single camera, or single/multiple neural networks with two cameras concurrently
The image shows a Raspberry Pi 5 with an attached Raspberry Pi M.2 HAT+ board. The Raspberry Pi 5 is the base component, identifiable by its HDMI ports, USB ports, and Ethernet port visible at the bottom right. The M.2 HAT+ board is mounted on top of the Raspberry Pi using four standoffs, which elevate it above the main board. The M.2 HAT+ board has an M.2 module installed, which is secured in place and connected to the HAT+ board. The setup appears to be compact and well-organized, with the M.2 module's connector edge visible and fitted into the HAT+ board. The ribbon cable is connected to the HAT+ board, indicating that it might be used for additional connectivity or power. This configuration is used to enhance the capabilities of the Raspberry Pi 5 by adding support for M.2 devices, which could include high-speed storage solutions or other peripherals, thus expanding the functionality and performance of the Raspberry Pi system.
Raspberry Pi 5 wearing the Raspberry Pi AI Kit

Hailo has created an extensive model zoo, where users can find a wide variety of pre-trained neural network models ready to deploy and optimised to run on the AI Kit.

The software

A significant hurdle in creating real-world AI-based vision applications is the software complexity of integrating the camera subsystem with the AI framework. We have worked hard to simplify this as much as possible. Our rpicam-apps suite of camera applications now has a post-processing template for integrating neural network inferencing running real-time in the camera pipeline. By using the pre-installed Hailo Tappas post-processing libraries, we are able to create advanced AI-based applications in only a few hundred lines of C++ code. Similar levels of integration into our Picamera2 framework will follow soon.

The image shows an M.2 module designed for use with a Raspberry Pi M.2 HAT+ or similar device. This specific module is a HAILO-8 AI processor, a specialized chip used for artificial intelligence and machine learning applications. The main features visible in the image are: HAILO-8 AI Processor: The large metallic shield in the center is the HAILO-8 AI processor, which is designed to accelerate deep learning and AI inference tasks. PCB Design: The module is mounted on a green printed circuit board (PCB) with various electronic components, including capacitors, resistors, and connectors. Gold Connector Edge: The bottom of the module features a gold edge connector, which fits into an M.2 slot. This connector includes multiple contacts for data transfer and power. Regulatory Labels: The module has a white label with regulatory information, including a QR code, an FCC mark, and other certification details. Electronic Components: Other smaller components are visible on the board, including a capacitor labeled "R22," which is likely part of the power regulation circuitry. This HAILO-8 M.2 module is used to provide advanced AI processing capabilities to devices like the Raspberry Pi, enabling them to perform complex machine learning tasks efficiently.
A closer look at the Hailo module

The software installation steps are very simple. Install a few packages through apt, reboot, and you are ready to try out some of our AI demos in a matter of minutes. The full set of instructions can be found in our getting started guide. Here’s a preview of some of our demos that you can run through rpicam-apps:

Object recognition: even works when traffic on the A14 is moving

Pose estimation: very casual, not at all suspicious

Object recognition: the lemon Coke “bottle” is controversial in these parts

Subject segmentation: one is an engineer, the other is a desk plant

With the Raspberry Pi AI Kit, you are not limited to using the Hailo-8L co-processor only in rpicam-apps or Picamera2. We also package an API integrated in the GStreamer framework and native Python or C/C++ applications. This also includes non-camera use cases, such as running inference on pre-recorded video files.

Further resources

Our documentation for the AI Kit is a great place to start.

For full technical specifications for the Hailo-8L AI accelerator module, visit Hailo’s product web page.

Hailo has created a set of advanced AI applications running on a Raspberry Pi 5. They also have a community forum for discussing topics specific to the Hailo-8L AI accelerator hardware and software tooling.