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
Nr produktu: 62198572

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

Nr produktu: 62198572

PLN 616

Price Details

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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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Bezpieczne pakowanie

100% oryginalne produkty

100% oryginalne produkty

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Co wyróżnia ten produkt

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.

Szczegóły Produktu

Powerful ML Inferencing with Google Coral USB Accelerator. Low power, high speed, and privacy-preserving inferencing for embedded AI devices. Get yours from Ubuy Poland
  • 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
Item Weight0.2 lbs (90 grams)

Dla kogo jest przeznaczony?

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.

  • Complex ML Tasks

    Ineffective for heavy-duty machine learning tasks that require powerful processing capabilities beyond its edge TPU limitations.

  • Budget-Conscious Buyers

    May not appeal to users looking for cost-effective solutions, as the product could be considered relatively expensive.

OPIS PRODUKTU

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!.

Masz jakieś zapytanie? Porozmawiaj z nami

Pytania i odpowiedzi klientów

  • pytanie: What is the Edge TPU?

    odpowiedź: 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.
  • pytanie: Which models does the Coral USB Accelerator support?

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

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

Google Coral Single-Board Computers & Accessories 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.

Opinie i oceny klientów

5.0
1 oceny klientów
  • 5 gwiazdka
    100%
  • 4 gwiazdka
    0%
  • 3 gwiazdka
    0%
  • 2 gwiazdka
    0%
  • 1 gwiazdka
    0%

Zrecenzuj ten produkt

Podziel się opinią z innymi klientami

Zalety

  • 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

Wady

  • Initial setup challenges with recognition and compatibility on certain systems

Platform Trust & Buyer Confidence

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4.3/5 9,000 + reviews
Read reviews
MT
Mohd
Verified buyer

“The product received very good packaging & safe…Thank You”

16 June 2026 · via Trustpilot
SJ
Shawati
Verified buyer

“Accurate delivery timing given”

16 June 2026 · via Trustpilot
YB
Youcef
Verified buyer

“Not madly expensive like I thought, and much quicker than promised.”

15 June 2026 · via Trustpilot
LM
Leila
Verified buyer

“Never dealt with Ubuy before, but everything worked out great. Seamless cross border purchasing and shipping. Thanks!”

6/7/2026 · via Trustpilot
KA
Kwame
Verified buyer

“The process was smooth, with clear communication and timelines. This was my 1st purchase and I am really impressed. I will definitely be coming back.”

12 June 2026 · via Trustpilot
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Ważne informacje

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