Key Features

• 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.
• 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.

Description

The Coral M.2 Accelerator is an M.2 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 almost 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 M.2 Accelerator is a dual-key M.2 card (either A+E or B+M keys), designed to fit any compatible M.2 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.

Specification

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Dimensions

Part List