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- Google Coral USB Edge TPU ML Beschleunigungsc...
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Sản phẩm #:
229165007
Google Coral USB Edge TPU ML Beschleunigungscoprozessor für Raspberry Pi und andere eingebettete Einzelboard-Computer
Sản phẩm #:
229165007
VND 4759447
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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.
Thông tin chi tiết sản phẩm
- 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 RAM | 2 KB |
| Kapazitu00e4t des Arbeitsspeichers | 16 KB |
| Prozessorgeschwindkeit | 32 MHz |
| Netzwerkverbindung | USB |
| Betriebssystem | Linux |
| Prozessormarke | ARM |
| Kompatible Geru00e4te | PC, Laptop, Raspberry Pi, andere Linux-Geru00e4te |
| RAM-Speichertechnik | LPDDR |
| Prozessoranzahl | 1 |
| Anzahl USB-Ausgu00e4nge | 1 |
| Smart-Home-Kompatibilitu00e4t | Nicht Smart-Home-kompatibel |
| Artikel Abmessungen L x B x H | 7,6L x 5,1B x 2,5H cm |
| Marke | Pelbaikim |
| Modellname | Coral-USB-Accelerator |
| UPC | 608614201389 |
| Hersteller-Modellnummer | Coral-USB-Accelerator |
| Hersteller-Teilenummer | Coral-USB-Accelerator |
| Hersteller | Google Coral |
| Anzahl von Einheiten | 1.0 stu00fcck |
| Gru00f6u00dfe des installierten RAM-Speichers | 2 KB |
| Speicherkapazitu00e4t | 16 KB |
| CPU-Taktfrequenz | 32 MHz |
Who Should Buy?
-
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.
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Casual Users
Not suitable for casual users with no technical background or experience in machine learning and programming.
MÔ TẢ SẢN PHẨM
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!.
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Câu hỏi và trả lời của khách hàng
-
câu hỏi:
What is the Edge TPU?
trả lời: 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. -
câu hỏi:
Which models does the Coral USB Accelerator support?
trả lời: It fully supports MobileNet and Inception architectures though custom architectures are possible. -
câu hỏi:
What is included in the package?
trả lời: 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. **
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ưu
- 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.
Nhược điểm
- The host computer must have adequate performance to support the TPU.
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VND 4759447
Đặt hàng ngay bây giờ và nhận hàng vào khoảng Thứ Bảy, Tháng Tám 29
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Các tính năng và lợi ích
- 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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