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
ერთეულის #: 229165007

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

ერთეულის #: 229165007

GEL 542

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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.

Პროდუქტის აღწერილობა

Shop USB Edge TPU ML Accelerator Coprocessor for Raspberry Pi and Other Embedded Single Board Computers online at a best price in Georgia. 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
RAM Memory Installed2 KB
Memory Storage Capacity16 KB
Processor Speed32 MHz
Network Connectivity TechnologyUSB
Operating SystemLinux
Processor BrandARM
Compatible DevicesPC, Laptop, Raspberry Pi, Other Linux Devices
RAM Memory TechnologyLPDDR
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
Number of Items1
UPC608614201389
Unit Count1.0 stu00fcck
ManufacturerGoogle Coral
Model NumberCoral-USB-Accelerator
Manufacturer Part NumberCoral-USB-Accelerator
BrandGoogle Coral
RAM memory installed size2 KB
CPU speed32 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.

პროდუქტის აღწერილობა

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!.

გაქვთ რაიმე შეკითხვა? მოგვმართეთ ჩათში

მომხმარებელთა კითხვები და პასუხები

  • კითხვა: What is the Edge TPU?

    პასუხი: 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.
  • კითხვა: Which models does the Coral USB Accelerator support?

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

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

GoogleCoral USB Cables Coral-USB-Accelerator Editorial Review

USB Edge TPU ML Accelerator Coprocessor For Raspberry Pi And Other Embedded Single Board Computers is an essential upgrade for optimizing machine learning applications on Raspberry Pi systems. Users report a significant performance boost, particularly in AI tasks such as object detection, while the compact design ensures easy integration with existing setups. The installation process is straightforward, allowing users to quickly connect via USB without complex configurations. The device provides efficient CPU load management, making it ideal for continuous use in projects like local camera surveillance and image recognition. Overall, it supports various compatible devices and plays well with popular software like Home Assistant and Frigate, enhancing local AI processing capabilities.

Customer Reviews & Ratings

1 მომხმარებლის შეფასება
  • 5 ვარსკვლავი
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  • 4 ვარსკვლავი
    0%
  • 3 ვარსკვლავი
    0%
  • 2 ვარსკვლავი
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  • 1 ვარსკვლავი
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ამ პროდუქტის მიმოხილვა

გაუზიარეთ თქვენი აზრები სხვა მომხმარებლებს

Დადებითი

  • Compact design fits well with small projects
  • Easy installation via USB connection
  • Significantly reduces CPU workload
  • Compatible with multiple Linux devices
  • Excellent for AI and image recognition
  • Stable performance even under continuous use

მინუსები

  • Requires compatible software for optimal function

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GEL 542

შეუკვეთეთ ახლავე და მიიღეთ Monday, სექტემბერი 28

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მახასიათებლები და უპირატესობები

  • 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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