The Coral USB Accelerator adds a Coral Edge TPU to your Linux, Mac, or Windows computer so you can accelerate your machine learning models. This page is your guide to get started.
All you need to do is download the Edge TPU runtime and PyCoral library. Then we'll show you how to run a TensorFlow Lite model on the Edge TPU.
To learn more about the hardware, see the USB Accelerator datasheet.
Requirements
A computer with one of the following operating systems:
Linux Debian 10, or a derivative thereof (such as Ubuntu 18.04), and a system architecture of either x86-64, Armv7 (32-bit), or Armv8 (64-bit) (includes support for Raspberry Pi 3 Model B+, Raspberry Pi 4, and Raspberry Pi Zero 2)
macOS 10.15 (Catalina) or 11 (Big Sur), with either MacPorts or Homebrew installed
Windows 10
One available USB port (for the best performance, use a USB 3.0 port)
Python 3.6 - 3.9
The Raspberry Pi AI Kit bundles the Raspberry Pi M.2 HAT+ with a Hailo AI accelerationmodule for use with Raspberry Pi 5. It provides an accessible, cost-effective, and powerefficient way to integrate high-performance AI. Explore applications including processcontrol, security, home automation, and robotics. Contains:
• a Hailo AI module containing a Neural Processing Unit (NPU)• a Raspberry Pi M.2 HAT+, to connect the AI module to your Raspberry Pi 5• a thermal pad pre-fitted between the module and the M.2 HAT+• a mounting hardware kit• a 16mm stacking GPIO header
The AI module is a 13 tera-operations per second (TOPS) neural network inferenceaccelerator 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 edgeconnector. The M.2 HAT+ communicates between the AI module’s M.2 interface and theRaspberry 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 NPU available for AI computingtasks. The built-in rpicam-apps camera applications in Raspberry Pi OS natively supportthe AI module, automatically using the NPU to run compatible post-processing tasks.
