Features & Benefits
- Miniature hardware platform for deep learning development
- Movidius vision processing unit (VPU) used
- No cloud connectivity required
- Not many accessories required
- No need for additional power supply, no heat sink or fan required
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7123880
Intel NCSM2450.DK1 Movidius Neural Compute Stick
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Item #:
7123880
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Prototype, tune, validate and deploy deep neural networks at the edge
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What Stands Out
Real-Time AI Processing
The Intel NCSM2450.DK1 enables edge devices to perform real-time deep learning inference, allowing developers to deploy sophisticated AI applications efficiently at the edge without needing constant cloud connectivity.
Compact Design
This Neural Compute Stick boasts a compact form factor, making it easy to integrate into projects without adding bulk, perfect for makers, developers, and tech enthusiasts looking for portable AI solutions.
Versatile Compatibility
Compatible with popular frameworks like TensorFlow and Caffe, the Intel Neural Compute Stick offers flexibility for diverse AI projects, ensuring users can utilize their preferred tools seamlessly for rapid development.
Product Details
- Neural Network Accelerator in USB Stick Form Factor
- Enables real-time on-device inference without cloud connectivity
- Does not require additional heat-sink, fan, cables, or power supply
- Designed for prototyping, tuning, validating, and deploying deep neural networks at the edge
- Utilizes the Movidius vision processing unit (VPU) in a miniature USB stick form
| Package Weight | 1 Pound |
Who Should Buy?
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AI Developers
Ideal for developers looking to integrate deep learning capabilities into their applications with ease.
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Edge Computing
Perfect for edge computing environments where low-power AI processing is essential without relying on cloud resources.
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Prototypers
Great for hardware prototyping, allowing quick and efficient AI implementations for testing and demos.
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High-End Users
Not suitable for those requiring advanced AI processing power beyond its low-power capabilities.
Product Description
Intel NCSM2450.DK1 Movidius Neural Compute Stick
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Customer Questions & Answers
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Question:
What is the Intel NCSM2450.DK1 Movidius Neural Compute Stick?
Answer: The Intel NCSM2450.DK1 Movidius Neural Compute Stick is a USB-based deep learning accelerator designed to enhance the performance of artificial intelligence applications. Powered by Intel's Movidius Myriad 2 Vision Processing Unit (VPU), this device enables edge computing, allowing users to run neural networks directly on devices without relying heavily on cloud processing. This is particularly useful for AI-driven applications in robotics, drones, and IoT edge devices, where real-time processing is crucial. It supports various deep learning frameworks, making it an essential tool for developers looking to integrate AI capabilities into their projects. -
Question:
What are the primary uses for the Movidius Neural Compute Stick?
Answer: The Movidius Neural Compute Stick is primarily used for running deep learning models at the edge of the network. It's ideal for applications like image recognition, object detection, and computer vision tasks in real-time environments. Developers can deploy complex neural networks for tasks like facial recognition in security systems or smart retail. With its ability to handle multiple workloads simultaneously, this device is perfect for projects that require immediate data processing on devices like drones, robotics, or smart cameras, freeing developers from latency issues associated with cloud computing. -
Question:
How do I set up the Intel Movidius Neural Compute Stick?
Answer: Setting up the Intel Movidius Neural Compute Stick is straightforward. First, connect the stick to a USB port on your computer or device. Then, install the required software, including the OpenVINO Toolkit, which allows developers to deploy their models efficiently. After that, you can start developing or importing your deep learning models. The stick is compatible with various operating systems and platforms, including Windows, Linux, and Raspberry Pi, enabling a wide range of applications, from desktop to embedded systems. -
Question:
What deep learning frameworks are compatible with the Movidius Neural Compute Stick?
Answer: The Intel Movidius Neural Compute Stick is compatible with several deep learning frameworks, most notably TensorFlow and Caffe, alongside other Model Optimization tools provided by Intel. This broad compatibility allows developers to convert existing models into a format that can be executed on the stick. For instance, with the OpenVINO Toolkit, you can optimize and deploy neural networks effortlessly, enhancing performance for applications such as image and speech recognition, making it a versatile choice for AI developers. -
Question:
Can the Neural Compute Stick run models in real-time?
Answer: Yes, the Intel Movidius Neural Compute Stick is designed for real-time inferencing, processing data as it comes in without noticeable lag. This feature is critically important for applications such as live video analysis, facial recognition, or anomaly detection in various fields like security, retail, and healthcare. The ability to run models efficiently at the edge reduces response times and empowers devices to make decisions swiftly without the need for cloud processing, thus ensuring that tasks are carried out more effectively. -
Question:
What types of models can I run on the Movidius Neural Compute Stick?
Answer: You can run a variety of deep learning models on the Intel Movidius Neural Compute Stick, including convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data processing. It is especially well-suited for vision-based applications like object detection and image classification. By optimizing and converting models from frameworks like TensorFlow or Caffe, you can deploy them to the stick to enhance device capabilities for use cases in robotics, augmented reality, and surveillance applications. -
Question:
Is the Neural Compute Stick suitable for beginners in AI development?
Answer: Absolutely! The Intel Movidius Neural Compute Stick is beginner-friendly, with extensive documentation and community support available. Developers can easily access sample projects and tutorials through the OpenVINO Toolkit, making it accessible for those new to AI development. Beginners can start with pre-trained models and gradually move towards building their custom implementations, making it a great way to learn about deep learning and AI concepts hands-on without needing high-end hardware. -
Question:
What are the power requirements for the Intel Movidius Neural Compute Stick?
Answer: The Intel Movidius Neural Compute Stick operates via a standard USB connection, drawing power directly from the USB port on your device. This means it requires no additional power supply and can run on any USB 2.0 or 3.0 port, making it highly versatile and portable for various applications. Its energy-efficient design ensures minimal power consumption, allowing it to operate effectively in battery-powered devices, such as drones and handheld gadgets, while extending their operational time. -
Question:
Where can I buy the Intel NCSM2450.DK1 Movidius Neural Compute Stick?
Answer: You can purchase the Intel NCSM2450.DK1 Movidius Neural Compute Stick from Ubuy. Ubuy offers a user-friendly shopping experience along with fast shipping times and excellent customer service. They provide a reliable platform to order the product you need, with various payment options available for convenience.
Intel Desktop Barebones Editorial Review
The Movidius Neural Compute Stick (NCS) is an AI specialized accelerator designed for deep neural network processing tasks. The device can be used to extend the processing power of a Raspberry Pi and can be used with audio and image data. The device is heavily image-centric and not intended as a magic stick to speed up everything on a computer. The device was reviewed positively for use with a laptop running Ubuntu and Raspberry Pi. The device was praised for its ability to detect classes of objects in images. However, it was also critically reviewed for being poorly supported, with unmaintained resources and unanswered issues. The lack of matured SDK creates limitations, especially for custom Tensorflow models.
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Pros
- Highly specialized for deep neural network processing tasks
- Enables a new generation of intelligent devices
- Can be used with audio and image data
- Helps recognize classes of objects in images
Cons
- Poorly supported with no maintenance and no reliable documentation
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