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Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer
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Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer
Coral USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer
Item #: 229165007

Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer

Item #: 229165007

NZD 318

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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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What Stands Out

Edge AI Processing
Accelerates machine learning tasks on embedded systems, enabling quick model inference and reduced latency for complex applications.
Seamless Integration
Easily connects to Raspberry Pi and other single-board computers, simplifying the implementation of AI in various tech projects.
Energy Efficient
Designed for low power consumption, allowing for high-performance operations without draining power, making it ideal for IoT devices.

Product Details

Shop Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer online at a best price in New Zealand. B07R53D12W
  • 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
Gru00f6u00dfe des installierten RAM2 KB
Kapazitu00e4t des Arbeitsspeichers16 KB
Prozessorgeschwindkeit32 MHz
NetzwerkverbindungUSB
BetriebssystemLinux
ProzessormarkeARM
Kompatible Geru00e4tePC, Laptop, Raspberry Pi, andere Linux-Geru00e4te
RAM-SpeichertechnikLPDDR
Prozessoranzahl1
Anzahl USB-Ausgu00e4nge1
Smart-Home-Kompatibilitu00e4tNicht Smart-Home-kompatibel
Artikel Abmessungen L x B x H7,6L x 5,1B x 2,5H cm
MarkePelbaikim
ModellnameCoral-USB-Accelerator
UPC608614201389
Hersteller-ModellnummerCoral-USB-Accelerator
Hersteller-TeilenummerCoral-USB-Accelerator
HerstellerGoogle Coral
Anzahl von Einheiten1.0 stu00fcck
Gru00f6u00dfe des installierten RAM-Speichers2 KB
Speicherkapazitu00e4t16 KB
CPU-Taktfrequenz32 MHz

Who Should Buy?

Suitable For
  • Machine Learning Enthusiasts

    Ideal for hobbyists experimenting with machine learning projects on Raspberry Pi due to its high processing capability.

  • Developers and Engineers

    Perfect for developers needing fast inference for AI applications in embedded systems and prototypes.

  • IoT Solutions Designers

    Great for those building IoT applications that require edge computing for real-time data processing.

Not Suitable For
  • Casual Users

    Not suitable for casual users with no technical background or experience in machine learning and programming.

Product Description

Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer

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!.

Have any Query? Chat with us

Product Buying Guide

The Google Coral USB Edge TPU ML Accelerator coprocessor is designed to bring powerful machine learning (ML) inferencing capabilities to existing Linux systems. It features the Edge TPU, a custom-made ASIC by Google, and provides high-performance ML inferencing with low power consumption over a USB 3.0 interface. This guide will help you explore the product's specifications, key features, usage scenarios, competitor comparison, user reviews, price analysis, and essential buying considerations to help you make an informed decision.

Product Specifications

  • Arm 32-bit Cortex-M0+ microprocessor (MCU): Up to 32 MHz max
  • 16 KB flash memory with ECC
  • 2 KB RAM
  • Connections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed)

Key Features

  • Google Edge TPU ML accelerator coprocessor
  • USB 3.0 Type-C socket
  • Supports Debian Linux on host CPU
  • Models are built using TensorFlow
  • Fully supports MobileNet and Inception architectures

Usage Scenarios

  • High-speed TensorFlow Lite inferencing
  • Low power consumption
  • Small footprint, ideal for embedded AI devices

Usage Scenarios

The Google Coral USB Edge TPU ML Accelerator coprocessor competes with similar products available in the market. These competitors offer alternative ML acceleration solutions with varying features and performance.

Some User Review

  • The speed and low power consumption of the Edge TPU accelerator have significantly improved my ML inferencing tasks.
  • The compatibility with Debian Linux and TensorFlow makes it a versatile and efficient ML inferencing solution.

Competitors

The Google Coral USB Edge TPU ML Accelerator coprocessor offers competitive pricing in the market, considering its cutting-edge ML inferencing capabilities, power efficiency, and Google's reliability. It provides excellent value for users seeking high-performance ML acceleration.

Buying Considerations

  • Ensure compatibility with your specific embedded system or single-board computer to maximize the benefits of the Edge TPU accelerator.
  • Evaluate the ML inferencing requirements of your projects to determine if the features and performance of the Edge TPU align with your needs.

Conclusion

With its powerful machine learning inferencing capabilities, low power consumption, and diverse usage scenarios, the Google Coral USB Edge TPU ML Accelerator coprocessor offers a compelling solution for embedded AI devices and ML projects. It's a versatile, reliable, and cost-effective choice for enhancing ML performance on existing Linux systems.

View Less

The Google Coral USB Edge TPU ML Accelerator coprocessor is designed to bring powerful machine learning (ML) inferencing capabilities to existing Linux systems. It features the Edge TPU, a custom-made ASIC by Google, and provides high-performance ML inferencing with low power consumption over a USB 3.0 interface. This guide will help you explore the product's specifications, key features, usage scenarios, competitor comparison, user reviews, price analysis, and essential buying considerations to help you make an informed decision. Continue Reading

Customer Questions & Answers

  • Question: What is the Edge TPU?

    Answer: 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?

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

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

Pelbaikim USB-Kabel Editorial Review

The Google Coral USB Edge TPU ML Accelerator has garnered a positive reception among users, particularly those leveraging it for AI-driven applications like surveillance and local machine learning tasks. Users report significant efficiency improvements when incorporating the TPU into their systems, particularly with software such as Frigate, Home Assistant, and Q Magic. The device is recognized for its ease of integration, with many users praising how straightforward the setup process is, especially in Linux environments such as Proxmox. One of the standout features is the substantial reduction in CPU load, allowing even older hardware to efficiently run complex machine learning models for tasks like object detection without diminishing performance. Reviewers highlight its effectiveness in parallel processing, managing multiple camera feeds seamlessly while maintaining optimal thermal performance—most users noted the device operates only slightly warm even under continuous use. Particularly appealing is its ability to run on low power while still delivering high performance, making it an excellent choice for users looking to implement local AI solutions without relying on cloud services. Its suitability for long-term use in a 24/7 operational context has also been confirmed, with users noting it has worked reliably for extended periods. **

Customer Reviews & Ratings

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Pros

  • Significant reduction in CPU load for AI tasks.
  • Easy to set up and integrate into existing systems.
  • High performance for local machine learning applications.
  • Low power Consumption and minimal heat generation.
  • Reliable operation in continuous use scenarios.

Cons

  • The host computer must have adequate performance to support the TPU.

Platform Trust & Buyer Confidence

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“Product came earlier than expected (shipped from US to NZ), in about 3 weeks. No damages and product was exactly as advertised!”

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“Easy to order, easy to track and the product is great to use. Also, the person-to-person (not automated) phone call I got to confirm that I'd received the item was wonderful ??”

11-Jul-26 · via Trustpilot
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NZD 318

Order now and get it around Friday, August 28

QTY:

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Features & Benefits

  • 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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