Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3
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Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3
Performs high-speed ML inferencing with on-board Edge TPU coprocessor capable of performing 4 trillion operations per second (TOPS), using 0.5 watts for each TOPS.
Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3
Numéro d'article: 19795334

Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3

Numéro d'article: 19795334

DJF 17561

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Performs high-speed ML inferencing with on-board Edge TPU coprocessor capable of performing 4 trillion operations per second (TOPS), using 0.5 watts for each TOPS.
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Ce qui se démarque

High Performance
The Edge TPU provides superior machine learning inference performance, enabling faster processing of complex tasks in both legacy and modern systems.
Versatile Compatibility
Integrates easily with new and existing systems through the M.2 interface, allowing for flexible deployment without extensive redesign.
Reduced Latency
Designed for edge computing, the SOM minimizes data transfer time, enhancing real-time decision-making capabilities essential for advanced applications.

Détails du produit

Shop for Google Coral System-On-Modules at Ubuy Djibouti. Integrate into legacy and new systems using M.2 A+E key interface. Performs high-speed ML inferencing with Edge TPU coprocessor. Supports TensorFlow Lite and AutoML Vision Edge. Works with Debian Linux.
Package Weight1.18 Pound

À qui est-ce destiné ?

Suitable For
  • Machine Learning Developers

    Ideal for developers seeking to integrate machine learning capabilities into their applications using Edge TPU.

  • Embedded System Engineers

    Perfect for engineers working on legacy systems wanting to enhance performance with advanced TPU capabilities.

  • IoT Device Manufacturers

    Beneficial for those creating IoT devices that require efficient ML processing at the edge for real-time data analysis.

Not Suitable For
  • Casual Users

    Not suitable for non-technical users without knowledge of hardware integration and machine learning concepts.

  • Basic Computing Needs

    Inadequate for users needing only simple computing tasks without the need for machine learning acceleration.

  • Budget-Conscious Consumers

    May not be appropriate for those looking for low-cost solutions, as specialized hardware often comes at a premium.

DESCRIPTION DU PRODUIT

Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is the Coral M.2 Accelerator AE Key?

    répondre: The Coral M.2 Accelerator AE Key is a compact ML compute accelerator designed to deliver high performance for machine learning tasks. Equipped with Google's Edge TPU, it allows for efficient processing of AI workloads directly on the device. This is particularly useful in edge computing scenarios where low latency and fast processing capabilities are required. For instance, it can be utilized in smart cameras for real-time image recognition, enabling applications like anomaly detection or object tracking.
  • question: What are the main features of the Coral M.2 Accelerator AE Key?

    répondre: The main features of the Coral M.2 Accelerator AE Key include its Edge TPU coprocessor, which provides accelerated inference for neural networks, and a compact M.2 2230 form factor that makes it suitable for integration with various devices. It also supports TensorFlow Lite, making it easier to deploy machine learning models. An example use case is enhancing IoT devices’ capacity to analyze data on-site, improving tasks such as predictive maintenance in industrial applications.
  • question: How does the Coral M.2 Accelerator improve machine learning performance?

    répondre: The Coral M.2 Accelerator enhances machine learning performance by providing specialized hardware designed specifically for neural network inference. It operates with low power consumption while delivering high throughput, making it ideal for applications requiring real-time processing without the need for data center resources. For instance, used in drones, it can process images for obstacle avoidance on-the-fly, which is critical for safe navigation.
  • question: What software compatibility does the Coral M.2 Accelerator AE Key offer?

    répondre: The Coral M.2 Accelerator AE Key is primarily compatible with TensorFlow Lite, enabling developers to easily train and deploy models tailored for edge devices. Furthermore, it supports various operating systems such as Linux and specific Raspberry Pi distributions. This compatibility ensures that developers can integrate AI into a range of applications, from smart home devices to advanced robotics, streamlining the development process.
  • question: Is the Coral M.2 Accelerator easy to install?

    répondre: Yes, the Coral M.2 Accelerator is designed for easy installation within M.2 2230 slots, which are commonly available on many modern motherboards and compute modules. Users can follow straightforward installation instructions to integrate the accelerator into their systems. For instance, if you're upgrading a media server for AI-enhanced streaming, ensuring the proper installation of the Coral Accelerator will allow quick access to AI data processing capabilities.
  • question: What types of projects can I use the Coral M.2 Accelerator for?

    répondre: The Coral M.2 Accelerator is versatile and can be used for various projects such as building smart cameras, developing gesture recognition systems, or creating real-time environmental monitoring solutions. It excels in applications that require immediate data processing and machine learning inference, such as robotics and automated inspections. For example, it can be integrated into a smart camera system for monitoring and analyzing people movement patterns in retail shops.
  • question: Can the Coral M.2 Accelerator work with Raspberry Pi?

    répondre: Yes, the Coral M.2 Accelerator is compatible with specific Raspberry Pi models through an M.2 adapter. This enables Raspberry Pi users to leverage enhanced AI processing capabilities in their projects. By adding the accelerator, developers can upgrade their Raspberry Pi projects, making them capable of running complex AI models. An example would be using it for a home automation system that controls lighting based on user patterns and preferences.
  • question: What power supply requirements are needed for the Coral M.2 Accelerator?

    répondre: The Coral M.2 Accelerator requires an external power supply that meets its voltage and current specifications, which typically involve 5V at a low current draw. Users must ensure their parent device can handle this requirement, which is common in many computing applications. For example, if integrated into a portable device, ensuring a reliable power source will allow for sustained performance during critical tasks such as mobile AI-driven analytics.
  • question: How can I update the firmware of the Coral M.2 Accelerator?

    répondre: To update the firmware of the Coral M.2 Accelerator, users typically need to connect it to a host device and follow the update process provided in the official documentation. This may involve downloading the latest firmware from the Coral website and using specific tools to apply the update. Keeping firmware updated is crucial to optimize performance and ensure compatibility with the latest AI models, thus enhancing the overall effectiveness of your machine learning applications.
  • question: Where can I buy the Coral M.2 Accelerator AE Key?

    répondre: You can purchase the Coral M.2 Accelerator AE Key (G650-04527-01) from Ubuy. Ubuy offers reliable shipping and customer service in Djibouti, making it a convenient choice for acquiring this cutting-edge ML compute accelerator. With Ubuy, you can also explore additional features and products conducive to your AI project needs, ensuring a comprehensive shopping experience.

GoogleCoral Acceleration Sensors Editorial Review

The Google Coral System-On-Modules (SOM) Edge TPU ML Compute Accelerator is a powerful tool for integrating Edge TPU into both legacy and new systems. With its M.2 A/E Key, it offers compatibility with a wide range of systems, including those with Linux and Windows operating systems. One of the standout features of this product is its compatibility with multiple systems. It can be operated on a 64-bit version of Debian 10 or Ubuntu 16.04 (or newer) for Linux systems, as well as on a 64-bit version of Windows 10 for Windows systems. This ensures flexibility and ease of use for users with different operating system preferences. Customers have found it easy to connect the Coral accelerator to their systems. However, it's important to note that the M.2 slot on this board is different from the standard NVME M.2 slot found on most motherboards. If the M.2 A/E Key slot is not available on your motherboard, an adapter such as a PCIe or M-Key adapter may be required. Some customers have raised concerns about compatibility with their specific setups. For example, if your Debian system only has a USB 3.0 and a PCIe 3.0 x4, it is recommended to get the USB Coral accelerator and connect it directly to a USB 3.0 port. This ensures seamless integration with your system. Overall, the Google Coral System-On-Modules (SOM) Edge TPU ML Compute Accelerator offers a powerful and flexible solution for integrating Edge TPU into different systems. With its compatibility with both Linux and Windows systems, users have the freedom to use it on their preferred operating system.

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Avantages

  • Compatible with both Linux and Windows systems
  • Flexible integration options with M.2 A/E Key slot or USB 3.0 port
  • Powerful performance for ML compute acceleration

Les inconvénients

  • May require an adapter for compatibility with certain motherboard setups

Historique des prix du produit

Informations importantes

  • Limitations : Pour les produits expédiés à l'international, veuillez noter que toute garantie du fabricant peut ne pas être valide ; les options de service du fabricant peuvent ne pas être disponibles ; les manuels, instructions et avertissements de sécurité des produits peuvent ne pas être dans les langues du pays de destination ; les produits (et les matériaux qui les accompagnent) peuvent ne pas être conçus conformément aux normes, spécifications et exigences d'étiquetage du pays de destination ; et les produits peuvent ne pas être conformes à la tension et aux autres normes électriques du pays de destination (nécessitant l'utilisation d'un adaptateur ou d'un convertisseur le cas échéant). Il incombe au destinataire de s'assurer que le produit peut être importé légalement dans le pays de destination. En cas de commande auprès d'Ubuy ou de ses filiales, le destinataire est l'importateur officiel et doit se conformer à toutes les lois et réglementations du pays de destination.
  • Tous les produits listés sur Ubuy ne sont pas à vendre, Ubuy étant un moteur de recherche mondial. Les produits sont soumis aux réglementations en matière d'exportation et de commerce.

DJF 17561

Commandez maintenant et recevez votre commande aux alentours du Sunday, Juillet 12

QTY:

Transaction sécuriséeTransaction sécurisée

Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

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Caractéristiques et avantages

  • On-board Edge TPU coprocessor performs high-speed ML inferencing
  • Capable of performing 4 trillion operations (tera-operations) per second (TOPS)
  • Uses 0.5 watts for each TOPS (2 TOPS per watt)
  • Can execute state-of-the-art mobile vision models such as MobileNet v2 at 400 FPS
  • Supports TensorFlow Lite without building models from the ground up
  • Works with Debian Linux and integrates with any Debian-based Linux system with a compatible card module slot