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

Introduction

Recently, the Raspberry Pi Foundation announced an exciting new AI kit that enhances AI capabilities on the Raspberry Pi 5, priced at just $ 70. This kit merges affordability with high performance, making it an appealing option for robot makers and creators seeking to leverage AI. Let’s take a closer look at what the Raspberry Pi AI Kit has to offer and how it compares with the Google Coral.

Overview of the Raspberry Pi AI Kit

The Raspberry Pi AI Kit includes the Raspberry Pi M2 hat combined with the Halo AI acceleration module specifically designed for use with the Raspberry Pi 5. This setup provides a cost-efficient and power-efficient method to integrate high-performance AI functionalities into projects.

In the box, users will find the following components:

Halo AI module
M2 hat
Mounting hardware
Stacking GPIO header

The Halo AI module features a neural processing unit (NPU) capable of delivering up to 13 trillion operations per second (TOPS) in inference performance. It connects to the Raspberry Pi 5 via a PCIe Gen 3 connection, allowing multiple cameras to utilize the inference engine simultaneously.

Comparison with Google Coral

When compared to the Google Coral, the Raspberry Pi AI Kit establishes itself as a superior option. The Google Coral can achieve only up to 4 TOPS, while the Halo delivers an impressive 13 TOPS, making it three times faster. Furthermore, the Coral’s performance is listed at 2 TOPS per watt, whereas the Halo module boasts a more efficient 3 TOPS per watt. Additionally, the Halo AI module provides broad support for various neural network frameworks, making it a more versatile choice than Coral, which is tightly connected with the TensorFlow Lite ecosystem.

Unboxing and Setup

Upon unboxing the AI Kit, the M2 hat is already pre-loaded with the Halo module, simplifying setup for users. The design includes a cutout for camera cables, making it easy to connect camera modules.

After assembling the components, a quick test confirmed that the camera was functioning correctly, providing real-time video at 30 frames per second. Current software libraries leverage the AI module to offload the processing workload from the main CPU, allowing it to handle other tasks efficiently.

The system demonstrated robust performance in detecting multiple objects and various models such as YOLO 5, YOLO 8, and YOLO X. Even though the detections were occasionally whimsical—detecting an overhead light as a toilet—the speed and fluidity of the performance were impressive.

Also noteworthy were the segmentation capabilities, effectively separating the user from the background, albeit with some noise. Pose estimation performance was also showcased, allowing movement detection in real-time.

Conclusion

In conclusion, the Raspberry Pi AI Kit offers an accessible, powerful alternative to the Google Coral, especially in terms of performance and efficiency. Priced affordably, it brings cutting-edge AI capabilities to users looking to experiment with machine learning applications on a budget.

Keywords

Raspberry Pi AI Kit
Google Coral
AI capabilities
Halo AI module
M2 hat
Neural Processing Unit (NPU)
TOPS (Terra Operations per Second)
TensorFlow Lite
Real-time video processing
Object detection
Segmentation
Pose estimation

FAQ

Q1: What is the price of the Raspberry Pi AI Kit?
A1: The Raspberry Pi AI Kit costs $ 70.

Q2: How much inference performance does the Halo AI module offer?
A2: The Halo AI module can perform up to 13 TOPS.

Q3: How does the performance of the Raspberry Pi AI Kit compare to Google Coral?
A3: The Raspberry Pi AI Kit is significantly faster, offering three times the performance of the Google Coral, which provides up to 4 TOPS.

Q4: What tools does the Raspberry Pi AI Kit support?
A4: The Halo AI module supports a broader range of neural network frameworks compared to Google Coral, which is primarily integrated with TensorFlow Lite.

Q5: Can the AI Kit handle multiple cameras?
A5: Yes, the Halo module can share its inference engine across multiple cameras concurrently.