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12460193
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
Numéro d'article:
12460193
CAD 231
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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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Ce qui se démarque
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.
Détails du produit
- 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
| 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 |
À qui est-ce destiné ?
-
AI Enthusiasts
Great for hobbyists experimenting with AI projects on Raspberry Pi or other embedded systems requiring on-device machine learning.
-
Developers
Ideal for software developers looking to prototype and deploy machine learning applications in a compact, efficient environment.
-
Robotics Engineers
Designed for robotics projects needing efficient computation power for image processing and real-time decision-making capabilities.
-
Casual Users
Not suitable for individuals seeking simple computing solutions without advanced machine learning applications or expertise.
DESCRIPTION DU PRODUIT
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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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.
Afficher moinsThe 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
Questions et réponses des clients
-
question:
What is the purpose of the USB Edge TPU ML Accelerator coprocessor?
répondre: 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. -
question:
What kind of projects can I build with the USB Edge TPU ML Accelerator?
répondre: 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. -
question:
How does the USB Edge TPU work with TensorFlow Lite?
répondre: 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. -
question:
Can the USB Edge TPU handle multiple machine learning models simultaneously?
répondre: 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. -
question:
Is the USB Edge TPU suitable for beginners in machine learning?
répondre: 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. -
question:
What power supply requirements are needed for the USB Edge TPU?
répondre: 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. -
question:
What are the main advantages of using the USB Edge TPU over CPU for ML tasks?
répondre: 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. -
question:
What programming languages can I use with the USB Edge TPU?
répondre: 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. -
question:
Will the USB Edge TPU work with all versions of Raspberry Pi?
répondre: 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. -
question:
Where can I buy USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers in Canada?
répondre: 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 Canada.
Google Coral Motherboards Coral-USB-Accelerator Editorial Review
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CAD 231
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Caractéristiques et avantages
- 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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