Coral Mini PCIe Accelerator

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Performs high-speed ML inferencing

The Coral Mini PCIe Accelerator is a PCIe module that brings the Edge TPU coprocessor to existing systems and products.

The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing with low power requirements: it's capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt). For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at 400 FPS, in a power efficient manner. This on-device processing reduces latency, increases data privacy, and removes the need for constant high-bandwidth connectivity.

The Mini PCIe Accelerator is a half-size Mini PCIe card designed to fit in any standard Mini PCIe slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways.

Works with Debian Linux

Integrates with any Debian-based Linux system with a compatible card module slot.

Supports TensorFlow Lite

No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge TPU.

Supports AutoML Vision Edge

Easily build and deploy fast, high-accuracy custom image classification models to your device with AutoML Vision Edge.


The Coral M.2 Accelerator must be connected to a host computer with the following specifications:

  • Any Linux computer with a compatible M.2 module slot
    • Debian 6.0 or higher, or any derivative thereof (such as Ubuntu 10.0+)
    • System architecture of either x86-64 or ARM32/64 with ARMv8 instruction set

For software required on the host, see the software and operation section.

  • Google Edge TPU ML accelerator
  • Standard Half-Mini PCIe card
  • Supports Debian Linux and other variants on host CPU


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