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5e4e1b63ce8f5c037b81b23c Coral Mini PCIe Accelerator https://cdn1.storehippo.com/s/59c9e4669bd3e7c70c5f5e6c/ms.products/5e4e1b63ce8f5c037b81b23c/images/5e4e1b63ce8f5c037b81b23d/5e4e1ae1c819aa5b2b06496b/webp/5e4e1ae1c819aa5b2b06496b.jpg

Description

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 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 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.

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.

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Coral Mini PCIe Accelerator

Description of product

Description

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 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 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.

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.