Coral M.2 Accelerator with Dual Edge TPU
The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module that brings two Edge TPU coprocessors to existing systems and products with an available M.2 E-key slot.
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PRODUCT DETAILS
The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module that brings two Edge TPU coprocessors to existing systems and products with an available M.2 E-key slot.
Features
- Performs high-speed ML inferencing: Each Edge TPU coprocessor is capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power. 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. With the two Edge TPUs in this module, you can double the inferences per second (8 TOPS) in several ways, such as by running two models in parallel or pipelining one model across both Edge TPUs.
- Works with Debian Linux and Windows: Integrates with Debian-based Linux or Windows 10 systems 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 with Dual Edge TPU is an M.2 module (E-key) that includes two Edge TPU ML accelerators, each with their own PCIe Gen2 x1 interface.
The Edge TPU is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power efficient manner: each one is capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power—that's 2 TOPS per watt. For example, one Edge TPU can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 frames per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.
With the two Edge TPUs in this module, you can double the inferences per second (8 TOPS) in several ways, such as by running two models in parallel or pipelining one model across both Edge TPUs.
Note
Because this module uses two PCIe x1 connections, it is not compatible with all M.2 E-key card slots. The dual Edge TPUs also result in special power requirements that you must carefully review.
Specifications
ML accelerator | 2x Google Edge TPU coprocessor: 8 TOPS (int8); 2 TOPS per watt |
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Hardware Interface | M.2 E-key (M.2-2230-D3-E) |
Serial Interface | Two PCIe Gen2 x1 |
Operating Voltage | 3.3V +/- 10% |
Storage Temperature | -40 to +85°C |
Operating Temperature | -40 to +85°C |
Relative Humidity | 0 to 90% (non-condensing) |
Dimensions
Part List
1 x Coral M2 Accelerator with Dual Edge TPU
LEARN AND DOCUMENTS
Documentations
Datasheet
Application notes
Software guides
- Model compatibility on the Edge TPU
- Edge TPU inferencing overview
- Run multiple models with multiple Edge TPUs
- Pipeline a model with multiple Edge TPUs
Downloads
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REVIEWS
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from order viewWorks very well.
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from order viewfinaly I got it!
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from order viewNot have idea how to install it on RPI
I buy it cause its 2 in 1 tpu