Is the new Raspberry Pi AI Kit better than Google Coral?

Kevin McAleer
4 Jun 202403:48

TLDRThe Raspberry Pi AI Kit, priced at $70, offers an affordable and efficient way to integrate AI capabilities with the Raspberry Pi 5, boasting a Halo AI acceleration module capable of 13 TOPS, surpassing Google Coral's 4 TOPS. The kit includes the Halo module, M2 hat, mounting hardware, and a stacking GPIO header. It supports multiple cameras and various AI models, demonstrating smooth operation at 30 frames per second with models like YOLOv5, YOLOv8, and pose estimation, making it a strong contender in the AI development space.

Takeaways

  • 🤖 Raspberry Pi has announced a new AI kit, bringing AI capabilities to the Raspberry Pi 5 for just $70.
  • 💡 The kit includes the Raspberry Pi M2 Hat and the Halo AI acceleration module for efficient AI performance.
  • 📦 In the box: Halo module, M2 Hat, mounting hardware, and a stacking GPIO header.
  • 🚀 The Halo module offers 13 TOPS (Tera Operations Per Second) of inference performance and connects via PCIe Gen 3.
  • 📷 The Halo module can handle multiple cameras simultaneously, enhancing AI functionality.
  • ⚖️ Compared to Google Coral, Halo performs at 13 TOPS, while Google Coral reaches 4 TOPS, with Halo being more power-efficient.
  • 🧠 Halo supports a broader range of neural network frameworks and excels in energy efficiency at 3 TOPS per watt.
  • 🔧 The video demonstrates unboxing, assembling the kit, and connecting cameras with easy setup features.
  • 📸 The AI kit runs object detection smoothly at 30 FPS, offloading AI tasks from the main CPU.
  • 🕺 Pose estimation and segmentation models are showcased, with accurate detection and real-time AI processing.

Q & A

  • What is included in the new Raspberry Pi AI kit?

    -The new Raspberry Pi AI kit includes the Raspberry Pi M2 hat, the Halo AI acceleration module, mounting hardware, and a stacking GPIO header.

  • How does the Halo AI module perform in terms of processing power?

    -The Halo AI module can perform up to 13 Terra operations per second (TOPS) of inference performance.

  • How does the Raspberry Pi AI kit compare to Google Coral in terms of performance?

    -The Raspberry Pi AI kit's Halo module performs up to 13 TOPS, whereas Google Coral performs up to 4 TOPS. Additionally, the Halo module is three times more efficient, offering 3 TOPS per watt compared to Coral’s 2 TOPS per watt.

  • What connectivity does the Halo module use with the Raspberry Pi 5?

    -The Halo module connects to the Raspberry Pi 5 via a PCIe Generation 3 connection.

  • What frameworks does the Halo module support compared to Google Coral?

    -The Halo module supports a broader range of neural network frameworks compared to Google Coral, which is more tightly integrated with the TensorFlow Lite ecosystem.

  • What is the price of the Raspberry Pi AI kit?

    -The Raspberry Pi AI kit costs $70.

  • How does the AI kit handle camera integration?

    -The AI kit has a convenient cut-out in the M2 hat for easy camera cable attachment, making it simple to connect cameras.

  • What AI tasks were demonstrated in the video using the Raspberry Pi AI kit?

    -The video demonstrated object detection, segmentation, and pose estimation, with the AI module smoothly handling multiple tasks simultaneously.

  • What specific AI models were used in the demonstration?

    -The YOLO 5, YOLO 8, and YOLO X models were used, along with a segmentation model and a pose estimation model.

  • How efficient is the AI kit in processing AI workloads?

    -The AI kit offloads AI tasks to the Halo module, allowing the Raspberry Pi’s main CPU to handle other tasks without lag. The demonstration ran at 30 frames per second, showing smooth detection of multiple objects at once.

Outlines

00:00

🤖 Raspberry Pi's New AI Kit Announcement

Raspberry Pi has introduced a new AI kit priced at $70, which enhances AI capabilities for the Raspberry Pi 5. The kit includes the M2 Hat and Halo AI acceleration module, offering an affordable and efficient way to integrate high-performance AI into projects. This kit is designed to compete with similar AI hardware like Google's Coral.

📦 What's Inside the AI Kit Box?

The AI kit includes several components: the Halo module, M2 Hat, mounting hardware, and a stacking GPIO header. The Halo module contains a Neural Processing Unit (NPU) capable of 13 TOPS (Trillion Operations Per Second), allowing it to process AI tasks efficiently, even across multiple cameras. The module connects via PCIe Gen 3, providing enhanced AI inference.

🆚 Comparing Halo AI Module and Google Coral

In comparison to the Google Coral, which offers 4 TOPS, the Halo module outperforms with its 13 TOPS. Google Coral integrates well with TensorFlow Lite and delivers 2 TOPS per watt, while the Halo module provides 3 TOPS per watt, making it more energy-efficient. The Halo module also supports a broader range of neural network frameworks, enhancing its versatility.

🛠️ Unboxing and Assembling the AI Kit

Along with the AI kit, the Raspberry Pi team has also sent a Raspberry Pi 5, power supply, pre-release software, and a Camera Module 3 for demo purposes. The Halo module is pre-attached to the M2 Hat, making setup easier. The AI kit also features a convenient cutout for connecting camera cables, facilitating seamless camera integration.

🎥 Testing the AI Kit with Camera Integration

Once assembled, the AI kit was tested with a small monitor to confirm camera functionality. The camera detected the user in real-time at 30 frames per second. The AI kit efficiently processes AI tasks, freeing up the main CPU for other functions. The demo also showcased the YOLO (You Only Look Once) models for object detection and segmentation tasks.

🔍 Object Detection and Segmentation with YOLO

The YOLO models were tested for object detection, with the AI identifying various items shown to the camera. Although the model inaccurately labeled a light as a 'toilet,' it performed well in detecting other objects and people. Additionally, segmentation and pose estimation models were tested, demonstrating the AI kit's ability to distinguish objects from the background and track human movement.

👋 Final Thoughts and Conclusion

The video concludes with a brief wrap-up of the AI kit's features and performance. The AI module smoothly handles multiple tasks and integrates various models. Despite some minor detection errors, the kit is efficient and user-friendly. The creator invites viewers to share their thoughts in the comments before signing off.

Mindmap

Keywords

💡Raspberry Pi AI Kit

The Raspberry Pi AI Kit is a newly announced product that brings AI capabilities to the Raspberry Pi 5. It is designed to be a cost-effective solution for AI processing and includes hardware components such as the Halo AI acceleration module and the M2 hat. The kit allows the Raspberry Pi to handle AI tasks like object detection and segmentation.

💡Google Coral

Google Coral is a competing AI hardware platform, known for its Tensor Processing Unit (TPU). It can perform up to 4 TOPS (Tera Operations Per Second) and is tightly integrated with TensorFlow Lite. In the video, it's compared to the Raspberry Pi AI Kit, which offers more TOPS and broader framework support.

💡TOPS

TOPS stands for Tera Operations Per Second, a measurement of computational performance, especially for AI inference tasks. In this video, the Raspberry Pi AI Kit’s Halo module performs up to 13 TOPS, while Google Coral can perform up to 4 TOPS, highlighting the performance difference.

💡Halo AI Acceleration Module

The Halo AI acceleration module is part of the Raspberry Pi AI Kit and handles AI processing tasks. It contains a neural processing unit (NPU) and can process up to 13 TOPS. This module supports multiple frameworks and performs AI inference tasks more efficiently compared to other systems like Google Coral.

💡Neural Processing Unit (NPU)

The NPU is a specialized hardware component designed to accelerate AI-related tasks, such as deep learning and neural network inference. In the video, the Halo AI module contains an NPU, allowing the Raspberry Pi to process AI tasks more efficiently without relying heavily on its CPU.

💡PCIe Generation 3

PCIe Generation 3 is a high-speed interface used for connecting peripheral devices to a computer or embedded system. The Raspberry Pi AI Kit uses this interface to connect the Halo AI module, enabling fast communication and efficient sharing of AI inference tasks between components like cameras and processors.

💡TensorFlow Lite

TensorFlow Lite is a lightweight version of Google’s TensorFlow, designed for mobile and embedded devices. The Google Coral platform is tightly integrated with TensorFlow Lite, allowing developers to deploy AI models efficiently on low-power devices. The Raspberry Pi AI Kit, in comparison, supports a broader range of frameworks.

💡YOLO (You Only Look Once)

YOLO is a popular real-time object detection algorithm. In the video, several versions of YOLO (YOLO 5, YOLO 8, and YOLO X) are mentioned as models that can be run on the Raspberry Pi AI Kit to detect objects in images or video streams.

💡Object Detection

Object detection is the process of identifying and locating objects within an image or video. The Raspberry Pi AI Kit, equipped with the Halo AI module, can perform real-time object detection tasks using models like YOLO at 30 frames per second, which allows it to identify multiple objects simultaneously.

💡Pose Estimation

Pose estimation is a computer vision technique used to determine the position of a person or object in an image or video, typically by identifying key points like limbs or joints. The Raspberry Pi AI Kit demonstrates pose estimation in the video, tracking the user’s movements in real-time.

Highlights

Raspberry Pi announces a new AI kit for the Raspberry Pi 5, priced at $70.

The AI kit includes the Raspberry Pi M2 Hat and the Halo AI acceleration module.

The Halo AI module can perform up to 13 TOPS (trillion operations per second) for AI inference.

The AI module connects to the Raspberry Pi 5 via a PCIe Generation 3 connection.

The AI kit supports multiple camera connections simultaneously.

Comparison with Google Coral: Halo AI module offers 13 TOPS, while Google Coral offers up to 4 TOPS.

The Halo AI module has 3 TOPS per watt, making it three times more power-efficient than Google Coral.

Broader support for new AI network frameworks compared to Google Coral.

The AI kit includes mounting hardware, stacking GPIO header, and other accessories.

Demonstration includes running the AI kit with a Raspberry Pi 5, power supply, and camera module.

The AI module effectively offloads AI tasks, allowing the main CPU to perform other tasks smoothly.

The AI module supports multiple AI models, such as YOLO 5, YOLO 8, and YOLO X.

Demonstration of object detection running at 30 frames per second with accurate real-time results.

Support for segmentation models and pose estimation, demonstrating real-time processing capabilities.

AI module performs smoothly, even with multiple objects detected simultaneously.