Price search results for Google Coral DEV Board Google Coral Dev Board 1 GB 4 x 1.5 GHz
The Google Coral Dev Board 4GB is a powerful single-board computer (SBC) with built-in Real-Time Inference module (Deep Learning /...read more
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The Google Coral Dev Board 4GB is a powerful single-board computer (SBC) with built-in Real-Time Inference module (Deep Learning / Machine Learning, EdgeTPU)The Coral Dev 4 GB is a development board based on Google Coral som (system-on-module) with 4GB RAM and 8GB eMMC Flash.
Unlike the Coral USB Accelerator, this development board is a stand-alone platform on which you can run your application completely.
With the Edge TPU, Tensor Flow Lite models can be used quickly and energy-saving for inference.
A particular advantage of this solution is that your data remains local.
This helps with latency and, of course, data protection.
Google is increasingly using artificial intelligence (AI) and machine learning (ML) to realize its services.
To do this, it developed specialized processors called TPU ("tensor processing Unit") for its data centers, which can execute the algorithms faster and more energy-saving with the TensorFlow Framework.
For example, Google Maps is enhanced by street-view street signs that are analyzed using a TensorFlow-based neural network.
The clou: TensorFlow can be easily programd in Python.
The Edge TPU supports the TensorFlow Lite framework.
The Edge TPU can perform up to 4 trillion arithmetic operations per second with only 2 W consumption.
TensorFlow Lite is a modified version of TensorFlow that has been specially adapted to the needs of mobile devices and embedded devices.
Many tensorFlow applications can also be implemented in TensorFlow Lite.
This text is machine translated.
Unlike the Coral USB Accelerator, this development board is a stand-alone platform on which you can run your application completely.
With the Edge TPU, Tensor Flow Lite models can be used quickly and energy-saving for inference.
A particular advantage of this solution is that your data remains local.
This helps with latency and, of course, data protection.
Google is increasingly using artificial intelligence (AI) and machine learning (ML) to realize its services.
To do this, it developed specialized processors called TPU ("tensor processing Unit") for its data centers, which can execute the algorithms faster and more energy-saving with the TensorFlow Framework.
For example, Google Maps is enhanced by street-view street signs that are analyzed using a TensorFlow-based neural network.
The clou: TensorFlow can be easily programd in Python.
The Edge TPU supports the TensorFlow Lite framework.
The Edge TPU can perform up to 4 trillion arithmetic operations per second with only 2 W consumption.
TensorFlow Lite is a modified version of TensorFlow that has been specially adapted to the needs of mobile devices and embedded devices.
Many tensorFlow applications can also be implemented in TensorFlow Lite.
This text is machine translated.