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Item #:
19795334
Coral M.2 Accelerator A+E Key,G650-04527-01 SOM- Edge TPU ML Compute Accelerator, M.2-2230-A-E-S3
Item #:
19795334
AED 357
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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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What Stands Out
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.
Product Details
- High-Performance ML Accelerator: Integrates Edge TPU, delivering 4 TOPS (int8) peak performance for machine learning inference tasks.
- Strong Compatibility: Supports M.2 A+E key interface for easy integration into existing systems.
- Low Power Design: Provides 2 TOPS per watt, ideal for embedded and energy-efficient applications.
- Wide OS Support: Compatible with Linux (Debian 10/Ubuntu 16.04+) and Windows 10 (64-bit).
- Industrial-Grade Reliability: Operating temperature range of -20°C to +85°C, suitable for harsh environments.
| Connectivity Technology | PCIe |
| Operating System | Debian Linux |
| Wireless Compability | Bluetooth |
| Compatible Devices | Laptops and desktops with M.2 (A+E key) slot |
| Processor Count | 1 |
| Brand | Google Coral |
| Model Name | G650-04527-01 (Coral M.2 Accelerator A+E Key) |
| UPC | 608614202768 |
| Model Number | G650-04527-01 |
| Mfr Part Number | G650-04527-01 |
| Manufacturer | Coral |
| Wireless Communication Standard | Bluetooth |
| Package Weight | 1.18 Pound |
Who Should Buy?
-
Machine Learning Developers
Ideal for developers seeking to integrate machine learning capabilities into their applications using Edge TPU.
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Embedded System Engineers
Perfect for engineers working on legacy systems wanting to enhance performance with advanced TPU capabilities.
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IoT Device Manufacturers
Beneficial for those creating IoT devices that require efficient ML processing at the edge for real-time data analysis.
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Casual Users
Not suitable for non-technical users without knowledge of hardware integration and machine learning concepts.
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Basic Computing Needs
Inadequate for users needing only simple computing tasks without the need for machine learning acceleration.
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Budget-Conscious Consumers
May not be appropriate for those looking for low-cost solutions, as specialized hardware often comes at a premium.
Product Description
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Customer Questions & Answers
-
Question:
What is the Coral M.2 Accelerator AE Key?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: 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?
Answer: You can purchase the Coral M.2 Accelerator AE Key (G650-04527-01) from Ubuy. Ubuy offers reliable shipping and customer service in UAE, 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.
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AED 357
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Features & Benefits
- 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
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