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Intel Movidius Neural Network Compute Stick
The Intel Movidius Neural Network Compute Stick is a compact USB-based accelerator that enables real-time deep learning inference on edge devices without requiring cloud connectivity or high-power GPU infrastructure. Machine learning engineers, computer vision developers, and IoT solution architects use this device to deploy pre-trained neural networks directly on embedded systems, robotics platforms, and smart cameras with minimal latency and power consumption. It solves the critical problem of bringing AI inference to the edge by providing hardware acceleration for convolutional neural networks while maintaining a form factor small enough to integrate into portable and resource-constrained applications.
Product Overview
The Intel Movidius Neural Network Compute Stick leverages Myriad X Vision Processing Unit (VPU) architecture to deliver specialized neural network acceleration through dedicated hardware rather than relying on general-purpose processors. The device connects via USB 3.0 interface and functions as a plug-and-play accelerator for Linux, Windows, and macOS systems. It features 16 programmable SHAVE cores optimized for parallel processing of convolutional operations, combined with dedicated hardware for image processing, motion estimation, and stereo depth computation. The Myriad X VPU achieves up to 4 TOPS (Tera Operations Per Second) of performance while consuming minimal power, making it ideal for battery-operated and thermally-constrained deployments.
What distinguishes the Movidius Neural Network Compute Stick is its support for multiple deep learning frameworks including TensorFlow, Caffe, MXNet, and Keras through the Intel OpenVINO toolkit, which provides optimized inference engines and model conversion utilities. The device includes 512 MB of LPDDR4 memory and 128 MB of on-chip SRAM, enabling efficient execution of quantized and optimized neural network models. Developers benefit from comprehensive software support including pre-built model zoo examples for object detection, image classification, pose estimation, and semantic segmentation, significantly reducing time-to-deployment for computer vision applications.
Key Specifications
| Specification | Details |
| Product Type | USB Neural Network Accelerator / Edge AI Compute Stick |
| Brand | Intel |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processing Unit | Intel Myriad X Vision Processing Unit (VPU) |
| SHAVE Cores | 16 programmable cores for parallel processing |
| Peak Performance | 4 TOPS (Tera Operations Per Second) |
| Memory Configuration | 512 MB LPDDR4 + 128 MB on-chip SRAM |
| Interface | USB 3.0 Type-A connector |
| Power Consumption | Typically 1-2.5W during inference |
| Supported Frameworks | TensorFlow, Caffe, MXNet, Keras, ONNX via OpenVINO |
| Operating Systems | Linux, Windows 10, macOS |
| Model Quantization | Support for INT8 and FP16 precision models |
Key Features
- Myriad X VPU Architecture: 16 SHAVE cores deliver 4 TOPS of peak performance, enabling real-time inference on complex neural networks with sub-100ms latency for object detection and image classification tasks
- Ultra-Low Power Consumption: Operates at 1-2.5W during active inference, making it ideal for battery-powered edge devices, drones, and mobile robotics that require extended operational runtime
- OpenVINO Toolkit Integration: Comprehensive software framework supporting model conversion from TensorFlow, Caffe, and PyTorch, with optimized inference engines and pre-trained model zoo for rapid prototyping
- Plug-and-Play USB Connectivity: USB 3.0 interface eliminates need for external power supplies or complex integration, enabling deployment on laptops, embedded Linux boards, and edge gateways within minutes
- Quantization and Model Optimization: Native support for INT8 and FP16 precision inference reduces model size by up to 75 percent while maintaining accuracy, enabling deployment of sophisticated neural networks on memory-constrained platforms
- Dedicated Vision Processing Hardware: Built-in image processing pipelines, motion estimation engines, and stereo depth processors accelerate computer vision preprocessing tasks without consuming general-purpose compute resources
Applications and Use Cases
- Real-Time Object Detection on Edge Cameras: Deploy YOLO, SSD, or Faster R-CNN models on industrial surveillance systems and smart cameras for person detection, vehicle tracking, and anomaly detection without cloud latency or bandwidth constraints
- Autonomous Mobile Robotics: Accelerate visual perception pipelines on wheeled and aerial robots for obstacle avoidance, path planning, and autonomous navigation using real-time depth estimation and semantic segmentation
- Medical Imaging Analysis: Execute chest X-ray classification, tumor detection, and diagnostic assistance models on portable medical devices and point-of-care systems with HIPAA-compliant on-device processing
- Smart City Infrastructure: Deploy traffic flow analysis, parking occupancy detection, and crowd monitoring models on edge gateways and smart poles for intelligent urban management without centralized data transmission
- Industrial Quality Control: Run visual inspection models on manufacturing assembly lines for defect detection, dimensional verification, and component classification with microsecond inference latency
- Retail Analytics: Implement customer counting, shelf monitoring, and product recognition models on in-store edge devices for inventory management and customer behavior analysis
How to Use
Begin by connecting the Intel Movidius Neural Network Compute Stick to an available USB 3.0 port on your host machine running Linux, Windows, or macOS. Install the Intel OpenVINO toolkit from the official Intel repository, which includes the necessary drivers, inference engine, and model optimizer tools. Download or prepare your pre-trained neural network model in a supported format such as TensorFlow frozen graphs, Caffe models, or ONNX format, then use the Model Optimizer utility to convert and quantize your model for the Myriad X architecture, generating optimized intermediate representation files.
Once your model is optimized, use the OpenVINO Inference Engine API with Python, C++, or Java bindings to load the model and execute inference on input images or video streams. The Compute Stick will automatically offload all neural network computations to the Myriad X VPU while your host CPU remains available for preprocessing, postprocessing, and application logic. For development and testing, leverage the included sample applications and pre-trained model zoo to understand the workflow before deploying your custom models. Monitor performance using the built-in profiling tools to optimize batch sizes, precision levels, and input resolutions for your specific latency and accuracy requirements.
Frequently Asked Questions
What neural network models can I deploy on the Movidius Neural Network Compute Stick?
The Compute Stick supports any convolutional neural network trained in TensorFlow, Caffe, MXNet, Keras, or ONNX format, provided the model has been optimized using the Intel Model Optimizer. Popular architectures including MobileNet, ResNet, VGG, Inception, YOLO, SSD, and Faster R-CNN are fully supported. The device excels with quantized models using INT8 precision, which reduces model size and improves throughput. While theoretically any CNN can be converted, models larger than 512 MB may require aggressive quantization or layer pruning to fit within the available memory constraints.
Can the Movidius Compute Stick run multiple models simultaneously or in sequence?
Yes, you can load multiple models sequentially and switch between them based on your application logic, though only one model executes at a time on the single VPU. The OpenVINO Inference Engine allows rapid model loading and unloading, enabling inference switching within milliseconds. For true parallel execution of multiple models, you would need multiple Compute Sticks connected via separate USB ports, each running independent inference pipelines. This approach is commonly used in multi-camera surveillance systems where each camera stream requires different detection models.
What is the typical inference latency for object detection models on the Movidius Compute Stick?
Inference latency depends on model architecture and input resolution, but typical values range from 30-100 milliseconds for optimized MobileNet-based object detectors running at 416x416 resolution. Lighter models like MobileNetV2 achieve 20-40ms latency, while more complex architectures like Faster R-CNN may require 150-300ms. The Compute Stick achieves these latencies while consuming only 1-2.5W of power, making it significantly more efficient than GPU-based inference for edge deployment scenarios.
Is the Movidius Compute Stick compatible with Raspberry Pi and other single-board computers?
Yes, the Compute Stick is fully compatible with Raspberry Pi 4, Jetson Nano, and other Linux-based single-board computers via USB 3.0 ports or powered USB hubs. The OpenVINO toolkit provides ARM-compatible binaries for these platforms. However, ensure your SBC has sufficient USB power delivery or use an externally powered USB hub, as the Compute Stick may draw up to 2.5A during peak inference operations. We recommend Raspberry Pi 4 with 4GB or 8GB RAM for optimal performance with complex models.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day
Buy Intel Movidius Neural Network Compute Stick Online in India
Purchase the Intel Movidius Neural Network Compute Stick online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.
Intel Movidius Neural Network Compute Stick
The Intel Movidius Neural Network Compute Stick is a compact USB-based accelerator that enables real-time deep learning inference on edge devices without requiring cloud connectivity or high-power GPU infrastructure. Machine learning engineers, computer vision developers, and IoT solution architects use this device to deploy pre-trained neural networks directly on embedded systems, robotics platforms, and smart cameras with minimal latency and power consumption. It solves the critical problem of bringing AI inference to the edge by providing hardware acceleration for convolutional neural networks while maintaining a form factor small enough to integrate into portable and resource-constrained applications.
Product Overview
The Intel Movidius Neural Network Compute Stick leverages Myriad X Vision Processing Unit (VPU) architecture to deliver specialized neural network acceleration through dedicated hardware rather than relying on general-purpose processors. The device connects via USB 3.0 interface and functions as a plug-and-play accelerator for Linux, Windows, and macOS systems. It features 16 programmable SHAVE cores optimized for parallel processing of convolutional operations, combined with dedicated hardware for image processing, motion estimation, and stereo depth computation. The Myriad X VPU achieves up to 4 TOPS (Tera Operations Per Second) of performance while consuming minimal power, making it ideal for battery-operated and thermally-constrained deployments.
What distinguishes the Movidius Neural Network Compute Stick is its support for multiple deep learning frameworks including TensorFlow, Caffe, MXNet, and Keras through the Intel OpenVINO toolkit, which provides optimized inference engines and model conversion utilities. The device includes 512 MB of LPDDR4 memory and 128 MB of on-chip SRAM, enabling efficient execution of quantized and optimized neural network models. Developers benefit from comprehensive software support including pre-built model zoo examples for object detection, image classification, pose estimation, and semantic segmentation, significantly reducing time-to-deployment for computer vision applications.
Key Specifications
| Specification | Details |
| Product Type | USB Neural Network Accelerator / Edge AI Compute Stick |
| Brand | Intel |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processing Unit | Intel Myriad X Vision Processing Unit (VPU) |
| SHAVE Cores | 16 programmable cores for parallel processing |
| Peak Performance | 4 TOPS (Tera Operations Per Second) |
| Memory Configuration | 512 MB LPDDR4 + 128 MB on-chip SRAM |
| Interface | USB 3.0 Type-A connector |
| Power Consumption | Typically 1-2.5W during inference |
| Supported Frameworks | TensorFlow, Caffe, MXNet, Keras, ONNX via OpenVINO |
| Operating Systems | Linux, Windows 10, macOS |
| Model Quantization | Support for INT8 and FP16 precision models |
Key Features
- Myriad X VPU Architecture: 16 SHAVE cores deliver 4 TOPS of peak performance, enabling real-time inference on complex neural networks with sub-100ms latency for object detection and image classification tasks
- Ultra-Low Power Consumption: Operates at 1-2.5W during active inference, making it ideal for battery-powered edge devices, drones, and mobile robotics that require extended operational runtime
- OpenVINO Toolkit Integration: Comprehensive software framework supporting model conversion from TensorFlow, Caffe, and PyTorch, with optimized inference engines and pre-trained model zoo for rapid prototyping
- Plug-and-Play USB Connectivity: USB 3.0 interface eliminates need for external power supplies or complex integration, enabling deployment on laptops, embedded Linux boards, and edge gateways within minutes
- Quantization and Model Optimization: Native support for INT8 and FP16 precision inference reduces model size by up to 75 percent while maintaining accuracy, enabling deployment of sophisticated neural networks on memory-constrained platforms
- Dedicated Vision Processing Hardware: Built-in image processing pipelines, motion estimation engines, and stereo depth processors accelerate computer vision preprocessing tasks without consuming general-purpose compute resources
Applications and Use Cases
- Real-Time Object Detection on Edge Cameras: Deploy YOLO, SSD, or Faster R-CNN models on industrial surveillance systems and smart cameras for person detection, vehicle tracking, and anomaly detection without cloud latency or bandwidth constraints
- Autonomous Mobile Robotics: Accelerate visual perception pipelines on wheeled and aerial robots for obstacle avoidance, path planning, and autonomous navigation using real-time depth estimation and semantic segmentation
- Medical Imaging Analysis: Execute chest X-ray classification, tumor detection, and diagnostic assistance models on portable medical devices and point-of-care systems with HIPAA-compliant on-device processing
- Smart City Infrastructure: Deploy traffic flow analysis, parking occupancy detection, and crowd monitoring models on edge gateways and smart poles for intelligent urban management without centralized data transmission
- Industrial Quality Control: Run visual inspection models on manufacturing assembly lines for defect detection, dimensional verification, and component classification with microsecond inference latency
- Retail Analytics: Implement customer counting, shelf monitoring, and product recognition models on in-store edge devices for inventory management and customer behavior analysis
How to Use
Begin by connecting the Intel Movidius Neural Network Compute Stick to an available USB 3.0 port on your host machine running Linux, Windows, or macOS. Install the Intel OpenVINO toolkit from the official Intel repository, which includes the necessary drivers, inference engine, and model optimizer tools. Download or prepare your pre-trained neural network model in a supported format such as TensorFlow frozen graphs, Caffe models, or ONNX format, then use the Model Optimizer utility to convert and quantize your model for the Myriad X architecture, generating optimized intermediate representation files.
Once your model is optimized, use the OpenVINO Inference Engine API with Python, C++, or Java bindings to load the model and execute inference on input images or video streams. The Compute Stick will automatically offload all neural network computations to the Myriad X VPU while your host CPU remains available for preprocessing, postprocessing, and application logic. For development and testing, leverage the included sample applications and pre-trained model zoo to understand the workflow before deploying your custom models. Monitor performance using the built-in profiling tools to optimize batch sizes, precision levels, and input resolutions for your specific latency and accuracy requirements.
Frequently Asked Questions
What neural network models can I deploy on the Movidius Neural Network Compute Stick?
The Compute Stick supports any convolutional neural network trained in TensorFlow, Caffe, MXNet, Keras, or ONNX format, provided the model has been optimized using the Intel Model Optimizer. Popular architectures including MobileNet, ResNet, VGG, Inception, YOLO, SSD, and Faster R-CNN are fully supported. The device excels with quantized models using INT8 precision, which reduces model size and improves throughput. While theoretically any CNN can be converted, models larger than 512 MB may require aggressive quantization or layer pruning to fit within the available memory constraints.
Can the Movidius Compute Stick run multiple models simultaneously or in sequence?
Yes, you can load multiple models sequentially and switch between them based on your application logic, though only one model executes at a time on the single VPU. The OpenVINO Inference Engine allows rapid model loading and unloading, enabling inference switching within milliseconds. For true parallel execution of multiple models, you would need multiple Compute Sticks connected via separate USB ports, each running independent inference pipelines. This approach is commonly used in multi-camera surveillance systems where each camera stream requires different detection models.
What is the typical inference latency for object detection models on the Movidius Compute Stick?
Inference latency depends on model architecture and input resolution, but typical values range from 30-100 milliseconds for optimized MobileNet-based object detectors running at 416x416 resolution. Lighter models like MobileNetV2 achieve 20-40ms latency, while more complex architectures like Faster R-CNN may require 150-300ms. The Compute Stick achieves these latencies while consuming only 1-2.5W of power, making it significantly more efficient than GPU-based inference for edge deployment scenarios.
Is the Movidius Compute Stick compatible with Raspberry Pi and other single-board computers?
Yes, the Compute Stick is fully compatible with Raspberry Pi 4, Jetson Nano, and other Linux-based single-board computers via USB 3.0 ports or powered USB hubs. The OpenVINO toolkit provides ARM-compatible binaries for these platforms. However, ensure your SBC has sufficient USB power delivery or use an externally powered USB hub, as the Compute Stick may draw up to 2.5A during peak inference operations. We recommend Raspberry Pi 4 with 4GB or 8GB RAM for optimal performance with complex models.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day
Buy Intel Movidius Neural Network Compute Stick Online in India
Purchase the Intel Movidius Neural Network Compute Stick online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.