USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
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
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
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: 12460193

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

89% des répondants recommanderaient ceci à un ami

Numéro d'article: 12460193

DJF 29551

DJF 31283

Price Details

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*All items will import from États-Unis

6%

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Il ne reste que 9 articles en stock.
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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

High Processing Power
Enhances machine learning performance significantly, allowing faster inference and more efficient data processing on Raspberry Pi and similar devices.
Compact Design
Designed for minimal space usage, making it ideal for embedded systems where size is a constraint without sacrificing performance.
Versatile Compatibility
Works seamlessly with various single board computers, broadening its application range and allowing diverse project implementations.

Détails du produit

Shop USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers online at a best price in Djibouti. B07R53D12W
  • Powerful ML inferencing capabilities with low power cost over USB 3.0
  • Executes state-of-the-art mobile vision models at 100+ fps
  • Developed in TensorFlow Lite and supports MobileNet and Inception architectures
  • High speed inferencing with low power consumption and small footprint
  • Built using Arm Cortex-M0+ Microprocessor with 16 KB Flash memory
  • Compatible with Google Cloud and supports Debian Linux on host CPU
Memory Storage Capacity16 KB
Connectivity TechnologyUSB
Operating SystemLinux
Processor BrandARM
Processor Count1
Total Usb Ports1
Item Dimensions L x W x H3"L x 2"W x 1"H (7.6 x 5.1 x 2.5 cm)
BrandGoogle-Coral
UPC608614201389
Model NumberCoral-USB-Accelerator
Mfr Part NumberCoral-USB-Accelerator
ManufacturerGoogle Coral
CPU ManufacturerARM
Package Weight1.0000 Pound

À qui est-ce destiné ?

Suitable For
  • AI Enthusiasts

    Great for hobbyists experimenting with AI projects on Raspberry Pi or other embedded systems requiring on-device machine learning.

  • Developers

    Ideal for software developers looking to prototype and deploy machine learning applications in a compact, efficient environment.

  • Robotics Engineers

    Designed for robotics projects needing efficient computation power for image processing and real-time decision-making capabilities.

Not Suitable For
  • Casual Users

    Not suitable for individuals seeking simple computing solutions without advanced machine learning applications or expertise.

  • High-Power Tasks

    Not ideal for users requiring heavy computational power, as it is designed for lightweight ML tasks on edge devices.

  • Non-Embedded Systems

    Not recommended for users looking to implement machine learning in standard desktops or laptops without compatibility.

DESCRIPTION DU PRODUIT

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 purpose of the USB Edge TPU ML Accelerator coprocessor?

    répondre: The USB Edge TPU ML Accelerator coprocessor is designed to enhance machine learning tasks on devices like the Raspberry Pi and other embedded single board computers. By offloading intense computation tasks from the main processor, it accelerates model inference, making it ideal for applications in computer vision, natural language processing, and real-time predictions. For instance, developers can deploy AI models for smart home devices or robotics systems, significantly improving their performance without consuming excessive resources.
  • question: What kind of projects can I build with the USB Edge TPU ML Accelerator?

    répondre: With the USB Edge TPU ML Accelerator, you can create a variety of innovative projects that require rapid AI processing. For instance, you could develop smart surveillance systems that utilize real-time object detection, or build interactive kiosks that provide personalized customer engagement based on machine learning models. Its capability to handle multiple TensorFlow Lite models makes it perfect for prototyping and deploying edge computing applications in sectors like healthcare, agriculture, and automation.
  • question: How does the USB Edge TPU work with TensorFlow Lite?

    répondre: The USB Edge TPU is optimized to work seamlessly with TensorFlow Lite, a lightweight version of Google’s machine learning framework. It accelerates the performance of TensorFlow Lite models by providing hardware features tailored for machine learning inference. When you convert your TensorFlow model to TensorFlow Lite, you can easily deploy it on the Edge TPU, allowing for quicker predictions and reduced latency, essential for applications like image recognition on mobile devices or robotics tasks requiring immediate feedback.
  • question: Can the USB Edge TPU handle multiple machine learning models simultaneously?

    répondre: Yes, the USB Edge TPU can manage multiple machine learning models at once, provided that the models are optimized for its architecture. Users can deploy several lightweight models and benefit from parallel processing capabilities. This feature is particularly beneficial in environments like smart cameras where different models might be analyzing various parameters simultaneously, such as detecting faces while processing gestures, thereby enhancing functionality without compromising performance.
  • question: Is the USB Edge TPU suitable for beginners in machine learning?

    répondre: Absolutely! The USB Edge TPU is beginner-friendly as it simplifies the process of deploying machine learning models on devices like the Raspberry Pi. With extensive documentation, community support, and example projects available from Google, beginners can easily integrate machine learning into their applications. Whether experimenting with image classification or voice recognition, it provides a great entry point for those wanting to explore AI without requiring deep technical expertise.
  • question: What power supply requirements are needed for the USB Edge TPU?

    répondre: The USB Edge TPU coprocessor typically operates via a USB connection, requiring power from the host device. Most Raspberry Pi models and compatible single board computers provide sufficient power through their USB ports. However, it’s good to ensure that the power supply can handle the additional load when multiple peripherals are connected. This consideration is particularly crucial in projects that involve multiple sensors or cameras, where power management becomes essential to maintaining system stability.
  • question: What are the main advantages of using the USB Edge TPU over CPU for ML tasks?

    répondre: The main advantages of using the USB Edge TPU for machine learning tasks include improved speed and efficiency. The Edge TPU is specifically designed for inferencing, allowing it to execute models faster than a general CPU could, especially useful for applications requiring quick decision-making like real-time video analysis. Additionally, it offers lower power consumption, which is critical for battery-operated devices and embedded systems that need to maintain long operational times.
  • question: What programming languages can I use with the USB Edge TPU?

    répondre: You can use Python APIs provided by TensorFlow Lite with the USB Edge TPU to build your machine learning applications. This supports various projects ranging from simple scripts to complex systems. Additionally, using C++ is also an option if you need more control over performance. These programming choices make it accessible for developers with different skill sets, letting them implement machine learning tasks easily on their preferred programming platform.
  • question: Will the USB Edge TPU work with all versions of Raspberry Pi?

    répondre: The USB Edge TPU is compatible with most Raspberry Pi models, specifically from Raspberry Pi 3 and above. It connects easily via the USB port, allowing you to leverage its accelerated processing capabilities without worrying about compatibility issues. This versatility enables users to enhance a wide range of projects, whether they’re working with the Raspberry Pi 3, 4, or other supported single board computers, ensuring you can take advantage of the latest machine learning advancements.
  • question: Where can I buy USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers in Djibouti?

    répondre: You can purchase the USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and other embedded single board computers from Ubuy. Ubuy provides a reliable platform for finding various electronics and components, including specialty items like the Edge TPU. It offers a convenient shopping experience with a range of delivery options, ensuring that you can easily obtain this innovative machine learning accelerator in Djibouti.

GoogleCoral Motherboards Coral-USB-Accelerator Editorial Review

  • ubuy Djibouti

The Google Coral USB accelerator is a simple device that is great for those with experience in software development. It has been successfully used in NVR/ML type applications without any failures. However, the product has been noted to be overpriced for what it is. The device is incredibly small, and the API docs and demos are easy to use. The product has encountered issues with running on certain platforms and has a steep learning curve. The quick start guide has not been effective in assisting with installation, but the device has been used successfully with Raspberry Pi 4. The product has received mixed reviews with some being very positive and some negative.

Avis et évaluations clients

4.1
514 évaluations des clients
  • 5 étoile
    65%
  • 4 étoile
    11%
  • 3 étoile
    8%
  • 2 étoile
    1%
  • 1 étoile
    15%

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Avantages

  • Simple to use
  • Small size
  • Great solution for Raspberry Pi 4

Les inconvénients

  • Overpriced

Historique des prix du produit

Informations importantes

  • Limitations : Pour les produits expédiés à l'international, veuillez noter que toute garantie du fabricant peut ne pas être valide ; les options de service du fabricant peuvent ne pas être disponibles ; les manuels, instructions et avertissements de sécurité des produits peuvent ne pas être dans les langues du pays de destination ; les produits (et les matériaux qui les accompagnent) peuvent ne pas être conçus conformément aux normes, spécifications et exigences d'étiquetage du pays de destination ; et les produits peuvent ne pas être conformes à la tension et aux autres normes électriques du pays de destination (nécessitant l'utilisation d'un adaptateur ou d'un convertisseur le cas échéant). Il incombe au destinataire de s'assurer que le produit peut être importé légalement dans le pays de destination. En cas de commande auprès d'Ubuy ou de ses filiales, le destinataire est l'importateur officiel et doit se conformer à toutes les lois et réglementations du pays de destination.
  • Tous les produits listés sur Ubuy ne sont pas à vendre, Ubuy étant un moteur de recherche mondial. Les produits sont soumis aux réglementations en matière d'exportation et de commerce.

DJF 29551

DJF 31283

Commandez maintenant et recevez votre commande aux alentours du Mardi, Juin 30

QTY:

Il ne reste que 9 articles en stock.
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Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

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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