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Nº de artículo:
12460193
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
87% of respondents would recommend this to a friend
Nº de artículo:
12460193
GTQ 1342
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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
High Processing Power
Enhances machine learning performance significantly, allowing faster inference and more efficient data processing on Raspberry Pi and similar devices.
Compact Design
Designed for minimal space usage, making it ideal for embedded systems where size is a constraint without sacrificing performance.
Versatile Compatibility
Works seamlessly with various single board computers, broadening its application range and allowing diverse project implementations.
Detalles de producto
- 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
| Memory Storage Capacity | 16 KB |
| Connectivity Technology | USB |
| Operating System | Linux |
| Processor Brand | ARM |
| Processor Count | 1 |
| Total Usb Ports | 1 |
| Item Dimensions L x W x H | 3"L x 2"W x 1"H (7.6 x 5.1 x 2.5 cm) |
| Brand | Google-Coral |
| UPC | 608614201389 |
| Model Number | Coral-USB-Accelerator |
| Mfr Part Number | Coral-USB-Accelerator |
| Manufacturer | Google Coral |
| CPU Manufacturer | ARM |
| Package Weight | 1.0000 Pound |
Who Should Buy?
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AI Enthusiasts
Great for hobbyists experimenting with AI projects on Raspberry Pi or other embedded systems requiring on-device machine learning.
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Developers
Ideal for software developers looking to prototype and deploy machine learning applications in a compact, efficient environment.
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Robotics Engineers
Designed for robotics projects needing efficient computation power for image processing and real-time decision-making capabilities.
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Casual Users
Not suitable for individuals seeking simple computing solutions without advanced machine learning applications or expertise.
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High-Power Tasks
Not ideal for users requiring heavy computational power, as it is designed for lightweight ML tasks on edge devices.
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Non-Embedded Systems
Not recommended for users looking to implement machine learning in standard desktops or laptops without compatibility.
DESCRIPCIÓN DEL PRODUCTO
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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Preguntas y respuestas de los clientes
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Pregunta:
What is the purpose of the USB Edge TPU ML Accelerator coprocessor?
Respuesta: The USB Edge TPU ML Accelerator coprocessor is designed to enhance machine learning tasks on devices like the Raspberry Pi and other embedded single board computers. By offloading intense computation tasks from the main processor, it accelerates model inference, making it ideal for applications in computer vision, natural language processing, and real-time predictions. For instance, developers can deploy AI models for smart home devices or robotics systems, significantly improving their performance without consuming excessive resources. -
Pregunta:
What kind of projects can I build with the USB Edge TPU ML Accelerator?
Respuesta: With the USB Edge TPU ML Accelerator, you can create a variety of innovative projects that require rapid AI processing. For instance, you could develop smart surveillance systems that utilize real-time object detection, or build interactive kiosks that provide personalized customer engagement based on machine learning models. Its capability to handle multiple TensorFlow Lite models makes it perfect for prototyping and deploying edge computing applications in sectors like healthcare, agriculture, and automation. -
Pregunta:
How does the USB Edge TPU work with TensorFlow Lite?
Respuesta: The USB Edge TPU is optimized to work seamlessly with TensorFlow Lite, a lightweight version of Google’s machine learning framework. It accelerates the performance of TensorFlow Lite models by providing hardware features tailored for machine learning inference. When you convert your TensorFlow model to TensorFlow Lite, you can easily deploy it on the Edge TPU, allowing for quicker predictions and reduced latency, essential for applications like image recognition on mobile devices or robotics tasks requiring immediate feedback. -
Pregunta:
Can the USB Edge TPU handle multiple machine learning models simultaneously?
Respuesta: Yes, the USB Edge TPU can manage multiple machine learning models at once, provided that the models are optimized for its architecture. Users can deploy several lightweight models and benefit from parallel processing capabilities. This feature is particularly beneficial in environments like smart cameras where different models might be analyzing various parameters simultaneously, such as detecting faces while processing gestures, thereby enhancing functionality without compromising performance. -
Pregunta:
Is the USB Edge TPU suitable for beginners in machine learning?
Respuesta: Absolutely! The USB Edge TPU is beginner-friendly as it simplifies the process of deploying machine learning models on devices like the Raspberry Pi. With extensive documentation, community support, and example projects available from Google, beginners can easily integrate machine learning into their applications. Whether experimenting with image classification or voice recognition, it provides a great entry point for those wanting to explore AI without requiring deep technical expertise. -
Pregunta:
What power supply requirements are needed for the USB Edge TPU?
Respuesta: The USB Edge TPU coprocessor typically operates via a USB connection, requiring power from the host device. Most Raspberry Pi models and compatible single board computers provide sufficient power through their USB ports. However, it’s good to ensure that the power supply can handle the additional load when multiple peripherals are connected. This consideration is particularly crucial in projects that involve multiple sensors or cameras, where power management becomes essential to maintaining system stability. -
Pregunta:
What are the main advantages of using the USB Edge TPU over CPU for ML tasks?
Respuesta: The main advantages of using the USB Edge TPU for machine learning tasks include improved speed and efficiency. The Edge TPU is specifically designed for inferencing, allowing it to execute models faster than a general CPU could, especially useful for applications requiring quick decision-making like real-time video analysis. Additionally, it offers lower power consumption, which is critical for battery-operated devices and embedded systems that need to maintain long operational times. -
Pregunta:
What programming languages can I use with the USB Edge TPU?
Respuesta: You can use Python APIs provided by TensorFlow Lite with the USB Edge TPU to build your machine learning applications. This supports various projects ranging from simple scripts to complex systems. Additionally, using C++ is also an option if you need more control over performance. These programming choices make it accessible for developers with different skill sets, letting them implement machine learning tasks easily on their preferred programming platform. -
Pregunta:
Will the USB Edge TPU work with all versions of Raspberry Pi?
Respuesta: The USB Edge TPU is compatible with most Raspberry Pi models, specifically from Raspberry Pi 3 and above. It connects easily via the USB port, allowing you to leverage its accelerated processing capabilities without worrying about compatibility issues. This versatility enables users to enhance a wide range of projects, whether they’re working with the Raspberry Pi 3, 4, or other supported single board computers, ensuring you can take advantage of the latest machine learning advancements. -
Pregunta:
Where can I buy USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers in Guatemala?
Respuesta: You can purchase the USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and other embedded single board computers from Ubuy. Ubuy provides a reliable platform for finding various electronics and components, including specialty items like the Edge TPU. It offers a convenient shopping experience with a range of delivery options, ensuring that you can easily obtain this innovative machine learning accelerator in Guatemala.
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Customer Reviews & Ratings
515 valoraciones de los clientes
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GTQ 1342
Haz tu pedido ahora y recíbelo por ahí Martes, Julio 07
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Secured transaction
Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
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características y beneficios
- 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