🎉 Up to 70% Off Selected ItemsShop Sale
HomeStoreBreakout Boards & Modules

Google Coral M.2 Accelerator A+E Key

Product image 1
Product image 2
Product image 3
Product image 4

Google Coral M.2 Accelerator A+E Key

Breakout Boards & Modules

The Coral M.2 Accelerator is an M.2 module that brings the Edge TPU coprocessor to existing systems and products with an available card module slot. Integrate the Edge TPU into legacy and new systems using an M.2 A+E key interface.

Also available in M.2 B+M key.

Performs high-speed ML inferencing

The on-board Edge TPU coprocessor is 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. See more performance benchmarks.

Works with Debian Linux and Windows

Integrates with any Debian-based Linux or Windows 10 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.

Specifications

  • ML accelerator: Google Edge TPU coprocessor: 4 TOPS (int8); 2 TOPS per watt
  • Connector: M.2 (A+E key)
  • Dimensions: 22 mm x 30 mm (M.2-2230-A-E-S3)

Resources

Application notes

Software Guides

API references

Downloads

$14.31

Original: $40.90

-65%
Google Coral M.2 Accelerator A+E Key—

$40.90

$14.31

Product Information

Shipping & Returns

Description

The Coral M.2 Accelerator is an M.2 module that brings the Edge TPU coprocessor to existing systems and products with an available card module slot. Integrate the Edge TPU into legacy and new systems using an M.2 A+E key interface.

Also available in M.2 B+M key.

Performs high-speed ML inferencing

The on-board Edge TPU coprocessor is 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. See more performance benchmarks.

Works with Debian Linux and Windows

Integrates with any Debian-based Linux or Windows 10 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.

Specifications

  • ML accelerator: Google Edge TPU coprocessor: 4 TOPS (int8); 2 TOPS per watt
  • Connector: M.2 (A+E key)
  • Dimensions: 22 mm x 30 mm (M.2-2230-A-E-S3)

Resources

Application notes

Software Guides

API references

Downloads