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Artikelnr.:
181267336
Google Coral USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
Artikelnr.:
181267336
ANG 268
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Onze beste bezorgdiensten
Coral USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
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Wat opvalt
High Performance
Accelerates machine learning tasks with exceptional speed, enabling real-time inference on edge devices, significantly enhancing the capabilities of Raspberry Pi and other embedded systems.
Compact ontwerp
Designed for space-constrained applications, this small coprocessor integrates seamlessly with embedded single-board computers, maintaining a clean and efficient architecture.
Veelzijdige Compatibiliteit
Compatible with various embedded platforms beyond Raspberry Pi, offering users the flexibility to implement machine learning across diverse projects and environments, maximizing investment potential.
Productdetails
- 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
| Geheugenopslagcapaciteit | 16 KB |
| Processor Speed | 32 MHz |
| Connectiviteitstechnologie | USB |
| Operating System | Linux |
| Processor Brand | ARM |
| RAM Memory Technology | LPDDR3 |
| Processor Count | 1 |
| Total Usb Ports | 1 |
| Merk | Pelbaikim |
| UPC | 608614201389 |
| Modelnummer | Coral-USB-Accelerator |
| Mfr-onderdeelnummer | Coral-USB-Accelerator |
| Fabrikant | Google Coral |
| CPU Speed | 32 MHz |
| Artikelgewicht | 0.2 lbs (90 gram) |
Voor wie is dit geschikt?
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AI Developers
Ideal for developers creating machine learning models and applications on Raspberry Pi and embedded systems.
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Hobbyist Projects
Perfect for hobbyists interested in integrating machine learning capabilities into their DIY electronics projects.
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Educational Use
Great for educators teaching AI concepts and hands-on machine learning in a classroom or workshop setting.
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Casual gebruikers
Not suited for general users looking for simple plug-and-play solutions without a technical background.
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Non-Embedded Applications
Inappropriate for users needing machine learning on standard PCs or laptops, where performance varies greatly.
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Limited Resources
Unsuitable for users without technical expertise or resources to manage and develop ML applications effectively.
PRODUCTBESCHRIJVING
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!.
Heeft u een vraag? Chat met ons
Vragen & Antwoorden van klanten
-
vraag:
What is the Edge TPU?
antwoord: 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. -
vraag:
Which models does the Coral USB Accelerator support?
antwoord: It fully supports MobileNet and Inception architectures though custom architectures are possible. -
vraag:
What is included in the package?
antwoord: The package includes the Coral USB Accelerator and a USB Type-C to Type-A cable.
Pelbaikim Single Board Computers Editorial Review
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Dit product wordt niet afgehandeld door Ubuy en de levertijd ervan kan minimaal 10 dagen bedragen. We kunnen het product uit de bestelling verwijderen en u terugbetalen als er zich een probleem voordoet met de levering ervan.
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Kenmerken en voordelen
- 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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