Raspberry Pi AI Kit SC1438

Raspberry PiSKU: ACC111

Price:
S$130
Stock:
In stock (12 units), ready to be shipped

Description

The Raspberry Pi AI Kit bundles the Raspberry Pi M.2 HAT+ with a Hailo AI acceleration module for use with Raspberry Pi 5. It provides an accessible, cost-effective, and power- efficient way to integrate high-performance AI. Explore applications including process control, security, home automation, and robotics.

The AI module is a 13 tera-operations per second (TOPS) neural network inference accelerator built around the Hailo-8L chip. The module uses the M.2 2242 form factor, and comes pre-installed in the M.2 HAT+, to which it connects through an M key edge connector. The M.2 HAT+ communicates between the AI module’s M.2 interface and the Raspberry Pi 5’s PCIe 2.0 interface.

When the host Raspberry Pi 5 is running an up-to-date Raspberry Pi OS image, it automatically detects the Hailo module and makes the Neural Processing Unit (NPU) available for AI computing tasks. The built-in rpicam-apps camera applications in Raspberry Pi OS natively support the AI module, automatically using the NPU to run compatible post-processing tasks.

 

Features

  • Hailo 8L accelerator with up to 13 TOPS (Trillions of Operations Per Second) inferencing performance.
  • Conforms to Raspberry Pi HAT+ specification.
  • Supplied pre-assembled on a Raspberry Pi M.2 HAT+ so ready out of the box to use with a Raspberry Pi 5.
  • Fully integrated into Raspberry Pi’s camera software stack.
  • Supplied with a 16mm stacking header and spacers/screws to enable the M.2 HAT+ to be fitted to a Raspberry Pi 5 with the Active Cooler in place.
  • Thermal pad pre-fitted between module and HAT+ spreads heat across components, improving performance

 

Includes

  • Hailo AI module containing a Neural Processing Unit (NPU)
  • Raspberry Pi M.2 HAT+, to connect the AI module to your Raspberry Pi 5
  • Thermal pad pre-fitted between the module and the M.2 HAT+
  • Mounting hardware kit
  • 16mm stacking GPIO header

 

Resources

 

Demonstrations from Raspberry Pi:

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