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

Entertainment

Introduction

Raspberry Pi has recently announced a new AI kit that brings AI capabilities to the Raspberry Pi 5, costing just $ 70. This kit includes the Raspberry Pi M2 HAT and the Halo AI acceleration module, offering a cost-effective and power-efficient way to integrate high-performance AI. Let's compare this new kit with the Google Coral AI board to see how they stack up.

Hey robot makers, the Raspberry Pi AI kit bundles the Raspberry Pi M2 HAT with the Halo AI acceleration module for use with a Raspberry Pi 5. The Halo module contains a neural processing unit that can perform up to 13 Terra operations per second of inference performance. It connects to the Raspberry Pi 5 via a PCIe Generation 3 connection and can share the inference engine across multiple cameras concurrently.

In comparison, the Google Coral can perform up to 4 TOPS, making it less powerful than the Halo module. The Coral is tightly integrated with the TensorFlow Lite ecosystem and performs at 2 TOPS per watt. On the other hand, the Halo module can perform up to 13 TOPS, making it three times faster than the Coral. Additionally, it is more efficient with three TOPS per watt and offers broader support for neural network frameworks.

The Raspberry Pi AI kit is priced at $ 70 and includes the Halo module, the Raspberry Pi M2 HAT, mounting hardware, and a stacking GPIO header. It provides a seamless way to implement high-performance AI on the Raspberry Pi platform.

Let's unbox the kit and assemble it. The kit comes with the Raspberry Pi 5, a power supply, a pre-loaded SD card with the software, the Halo module, and a camera module 3 for demos. The M2 HAT already has the Halo module attached, making it easy to set up with cameras. The software running on the Raspberry Pi includes updated libraries for AI processing, allowing for real-time object detection and more.

In a demonstration, the AI kit showcased its ability to detect objects at 30 frames per second, running smoothly without overburdening the main CPU. Various models like YOLO 5, YOLO 8, and YOLO X can be used for different types of detections. The segmentation model effectively distinguished subjects from the background, while the pose estimation model accurately captured movements.

In conclusion, the Raspberry Pi AI kit offers a compelling solution for integrating AI capabilities into projects at an affordable price, outperforming the Google Coral in terms of speed and efficiency.

Keywords

Raspberry Pi AI kit, Google Coral, Halo module, neural processing unit, TOPS, TensorFlow Lite, object detection, segmentation, pose estimation.

FAQ

  1. What does the Raspberry Pi AI kit include?
  2. How does the performance of the Halo module in the Raspberry Pi AI kit compare to Google Coral?
  3. What software features are included in the Raspberry Pi AI kit?
  4. How does the Raspberry Pi AI kit handle different AI models like YOLO and pose estimation?
  5. Is the Raspberry Pi AI kit a cost-effective solution for implementing AI on the Raspberry Pi platform?