USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
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USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
Coral USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
Numéro d'article: 62198572

USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

Numéro d'article: 62198572

MUR 7000

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Coral USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
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Ce qui se démarque

Fast Inference
Achieves high-speed machine learning inferencing with low power consumption, making it ideal for edge computing applications without sacrificing performance.
Easy Integration
Designed for seamless integration with Raspberry Pi and other single-board computers, simplifying setup for developers and hobbyists alike.
Versatile Applications
Supports a wide range of machine learning models, empowering users to create solutions in various fields like IoT, robotics, and smart devices.

Détails du produit

Powerful ML Inferencing with Google Coral USB Accelerator. Low power, high speed, and privacy-preserving inferencing for embedded AI devices. Get yours from Ubuy Mauritius
  • Featuring the Edge TPU, designed and built by Google
  • Provides high performance ML inferencing with low power cost over a USB 3.0 interface
  • Executes state-of-the-art mobile vision models at over 100 fps in a power-efficient manner
  • Allows fast ML inferencing to embedded AI devices in a power-efficient and privacy-preserving way
  • Models developed in TensorFlow Lite and then compiled to run on the USB Accelerator
  • Key benefits: High speed TensorFlow Lite inferencing, low power, small footprint
Memory Storage Capacity16 KB
Network Connectivity TechnologyUSB
Operating SystemLinux
Processor BrandARM
Processor Count1
Total USB Ports1
Item Dimensions L x W x H7.6L x 5.1W x 2.5H centimetres
Brand NameGoogle Coral
Model NameCoral-USB-Accelerator
UPC608614201389
Model NumberCoral-USB-Accelerator
Manufacturer Part NumberCoral-USB-Accelerator
ManufacturerGoogle Coral
BrandGoogle-Coral
Connectivity technologyUSB

À qui est-ce destiné ?

Suitable For
  • AI Developers

    Ideal for developers creating machine learning applications requiring fast inference on edge devices and single board computers.

  • Embedded System Hobbyists

    Perfect for hobbyists wanting to add AI capabilities to their Raspberry Pi and other embedded systems projects.

  • Robotics Engineers

    Useful for robotics engineers needing real-time object detection and classification with minimal latency in applications.

Not Suitable For
  • General Users

    Not suitable for casual users lacking programming skills or AI knowledge to leverage advanced machine learning capabilities.

DESCRIPTION DU PRODUIT

USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

About This Item

Introducing the Google Coral USB Edge TPU ML Accelerator - the ultimate coprocessor for Raspberry Pi and other embedded single board computers. This powerful device brings advanced machine learning (ML) inferencing capabilities to your existing Linux systems. Featuring the highly efficient Edge TPU, a small ASIC designed and developed by Google, the Coral USB Accelerator provides you with high-performance ML inferencing while consuming minimal power through a USB 3.0 interface. With its cutting-edge technology, this accelerator can execute state-of-the-art mobile vision models, such as MobileNet v2, at over 100 frames per second in a power-efficient manner. The Coral USB Accelerator allows you to enable fast ML inferencing on your embedded AI devices, all while maintaining a power-efficient and privacy-preserving approach.

Models are developed using TensorFlow Lite and then compiled to run seamlessly on this accelerator, providing you with high-speed inferencing capabilities. One of the key benefits of the Edge TPU is its ability to deliver low-power ML inferencing without compromising on performance. This coprocessor is equipped with an Arm 32-bit Cortex-M0+ Microprocessor (MCU) with up to 32 MHz clock speed, ensuring outstanding speed and efficiency. In addition to its impressive performance, the Coral USB Accelerator also boasts a small footprint, making it a flexible and versatile solution for your embedded systems. It comes with a USB 3.1 (gen 1) port and cable, ensuring a SuperSpeed data transfer rate of up to 5Gb/s. The Coral USB Accelerator is fully compatible with Google Cloud and supports Debian Linux on host CPUs.

You can develop models using TensorFlow and take advantage of its compatibility with popular architectures like MobileNet and Inception. Furthermore, the device supports custom architectures, opening up endless possibilities for your ML projects. At Ubuy, we offer a range of e-commerce options for Raspberry Pi and other embedded single board computers, allowing you to conveniently shop for high-quality embedded computing products and components. Whether you are an AI enthusiast, a hobbyist, or a professional developer, our online store provides you with a seamless shopping experience for all your embedded system needs. Discover the future of embedded AI with the Google Coral USB Edge TPU ML Accelerator.

Shop now and unlock the potential of your embedded systems!.

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is the Edge TPU?

    répondre: The Edge TPU is a small ASIC designed and built by Google that provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
  • question: Which models does the Coral USB Accelerator support?

    répondre: It fully supports MobileNet and Inception architectures though custom architectures are possible.
  • question: What is included in the package?

    répondre: The package includes the Coral USB Accelerator and a USB Type-C to Type-A cable.

Google Coral Single-Board Computers & Accessories Coral-USB-Accelerator Editorial Review

The Google Coral USB Edge TPU ML Accelerator coprocessor has received mixed feedback from users. While some have found it extremely easy to use and have seen significant improvements in performance, others have faced issues with the device failing or not being recognized initially. One user expressed frustration at the need to update drivers for compatibility. Nevertheless, those who got it up and running seamlessly praised its performance and the relief it brought to their CPU workload. Overall, when functioning correctly, the product has proven to be effective in accelerating machine learning inference tasks, such as object detection for CCTV systems on platforms like Raspberry Pi and Home Assistant. However, there are concerns about quality control and initial setup challenges for certain setups, which have impacted the user experience.

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Avantages

  • Significant performance improvement in machine learning inference tasks
  • Effective in offloading AI detection burden from the CPU
  • Compatible with platforms like Raspberry Pi, Home Assistant, and Frigate

Les inconvénients

  • Initial setup challenges with recognition and compatibility on certain systems

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MUR 7000

Commandez maintenant et recevez votre commande aux alentours du Samedi, Août 29

QTY:

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Conforme PCI DSS et certifié ISO 27001:2022, avec des paiements chiffrés et une protection complète de l'acheteur pour chaque commande.

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Caractéristiques et avantages

  • Brings powerful ML inferencing capabilities to existing Linux systems
  • Execute state-of-the-art mobile vision models in a power-efficient manner
  • Great for fast ML inferencing to embedded AI devices in a privacy-preserving way
  • Fully supports MobileNet and Inception architectures though custom architectures are possible
  • Compatible with Google Cloud
  • Features Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Debian Linux on host CPU and models built with TensorFlow

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