UP AI Core X powered by AI CORE Movidius Myriad X / XM / XP4 / XP8

The UP AI Core X is a high-performance edge AI accelerator board powered by Intel's Movidius Myriad X / XM / XP4 / XP8 Vision Processing Unit (VPU), engineered for real-time machine learning inference at the edge with minimal power consumption. Professional developers, roboticists, and embedded systems engineers use this platform to deploy deep neural networks for computer vision, object detection, pose estimation, and autonomous systems without relying on cloud infrastructure. This product solves the critical challenge of running sophisticated AI models locally on resource-constrained devices while maintaining sub-100ms latency and ultra-low power draw, making it ideal for battery-powered IoT and mobile applications.

Product Overview

The UP AI Core X represents a breakthrough in edge computing by integrating Intel's Movidius Myriad X / XM / XP4 / XP8 VPU architecture with the UP board ecosystem, delivering specialized hardware acceleration for convolutional neural networks and deep learning models. The Myriad VPU utilizes a 16-core VLIW processor architecture combined with dedicated neural compute engines, enabling parallel processing of multiple AI workloads simultaneously. This heterogeneous computing approach separates AI inference from general CPU operations, allowing the main processor to handle system tasks while the VPU operates independently at optimal efficiency, consuming as little as 1-2 watts during active inference compared to 25-50 watts for GPU-based solutions.

What distinguishes the UP AI Core X is its support for multiple Movidius generations (Myriad X, XM, XP4, XP8), providing flexibility in choosing performance tiers based on project requirements. The platform supports OpenVINO toolkit, TensorFlow Lite, PyTorch, and ONNX model formats, enabling seamless conversion and deployment of pre-trained models. With built-in support for 8-bit and 16-bit quantization, developers can compress large neural networks without significant accuracy loss, achieving inference speeds up to 4 TOPS (Tera Operations Per Second) while maintaining model precision. The unified development environment and comprehensive SDK make it accessible for both prototyping and production-scale deployments in surveillance, robotics, autonomous vehicles, and industrial automation.

Key Specifications

Specification	Details
Product Type	Edge AI Accelerator Board with Movidius VPU
Brand	UP Squared / UP Board with Intel Movidius
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
VPU Architecture	16-core VLIW with dedicated neural compute engines
Peak Performance	Up to 4 TOPS (Tera Operations Per Second)
Power Consumption	1-2 watts during active inference
Supported Model Formats	TensorFlow, PyTorch, ONNX, Caffe, MXNet
Quantization Support	8-bit and 16-bit integer quantization
Development Tools	OpenVINO Toolkit, Intel Neural Compute Stick SDK

Key Features

• Dual VPU Architecture: Supports parallel processing with Myriad X / XM / XP4 / XP8 variants, enabling simultaneous inference of multiple neural networks for complex AI pipelines
• Ultra-Low Power Consumption: Achieves 1-2 watts during inference compared to 25-50 watts for GPU alternatives, extending battery life in mobile and IoT applications
• Sub-100ms Latency: Real-time inference capability for time-critical applications including autonomous driving, robotics, and live video analytics
• Model Compression and Quantization: Built-in support for 8-bit and 16-bit quantization reduces model size by up to 75% without significant accuracy degradation
• OpenVINO Toolkit Integration: Unified framework for model optimization, conversion, and deployment across heterogeneous hardware platforms
• Multiple Connectivity Options: PCIe, USB 3.0, and Ethernet interfaces for seamless integration with existing systems and sensor networks

Applications and Use Cases

• Autonomous Robotics: Deploy real-time object detection, semantic segmentation, and pose estimation models on mobile robots for navigation and manipulation tasks without cloud dependency
• Video Surveillance and Analytics: Process multiple video streams simultaneously for person detection, behavior analysis, and anomaly detection at the edge with minimal latency
• Industrial IoT and Predictive Maintenance: Run machine learning models on edge devices to analyze sensor data, predict equipment failures, and optimize production processes in real-time
• Smart City and Traffic Management: Implement vehicle counting, license plate recognition, and traffic flow optimization directly on edge hardware deployed at intersections and highways
• Medical Imaging and Diagnostics: Accelerate inference for medical image analysis, pathology detection, and diagnostic support systems in hospital PACS and portable medical devices
• Drone and UAV Applications: Enable onboard AI processing for autonomous flight, target detection, and real-time decision-making without relying on ground-based servers

How to Use

Begin by installing the OpenVINO toolkit on your UP AI Core X board, which provides the complete development environment for model optimization and deployment. Convert your pre-trained neural network model (from TensorFlow, PyTorch, or ONNX format) to OpenVINO's Intermediate Representation (IR) format using the Model Optimizer tool, which automatically applies quantization and layer fusion optimizations specific to the Movidius VPU architecture. Connect your input source (USB camera, video file, or sensor stream) and initialize the Inference Engine with your optimized model, then execute inference on the VPU while the main CPU remains available for application logic and system tasks.

For production deployments, leverage the Intel Neural Compute Stick SDK to package your inference pipeline with minimal dependencies, enabling rapid scaling across multiple edge devices. Configure power management settings to optimize performance-per-watt based on your application's latency requirements, and implement error handling and model fallback mechanisms for robust operation in field conditions. Monitor VPU utilization and thermal performance using the provided diagnostic tools, and regularly update your models as new training data becomes available using the continuous learning framework built into the OpenVINO ecosystem.

Frequently Asked Questions

What is the difference between Movidius Myriad X, XM, XP4, and XP8 variants?

Movidius Myriad X is the original generation with 16-core VLIW architecture delivering up to 4 TOPS. Myriad XM offers improved thermal management and power efficiency. Myriad XP4 and XP8 represent next-generation variants with enhanced neural compute engines, supporting higher precision operations and larger model sizes. XP8 provides the highest performance tier with 8 TOPS capability, making it suitable for demanding applications like real-time 4K video processing and multi-model inference pipelines.

Can I run multiple AI models simultaneously on the UP AI Core X?

Yes, the dual VPU architecture and 16-core VLIW processor enable parallel execution of multiple neural networks. You can partition the VPU resources between different models or execute them in a time-multiplexed fashion depending on your latency requirements. The OpenVINO toolkit provides APIs for managing multiple inference requests and scheduling them efficiently across available compute resources.

What model formats are supported, and how do I convert my existing models?

The UP AI Core X supports TensorFlow, PyTorch, Caffe, MXNet, and ONNX model formats through the OpenVINO Model Optimizer. The conversion process automatically applies quantization, layer fusion, and hardware-specific optimizations. You can convert models using command-line tools or Python APIs, and the toolkit provides visualization tools to verify model structure and layer compatibility before deployment.

What is the power consumption during inference, and how does it compare to GPUs?

The UP AI Core X consumes approximately 1-2 watts during active inference, compared to 25-50 watts for NVIDIA Jetson or discrete GPUs. This 10-25x power advantage makes it ideal for battery-powered applications, IoT edge devices, and scenarios where thermal management is critical. Peak power consumption during model loading and initialization may reach 3-5 watts, but sustained inference remains in the 1-2 watt range.

Is quantization mandatory, and will it reduce model accuracy?

Quantization is optional but recommended for optimal performance and power efficiency. The UP AI Core X supports 8-bit and 16-bit quantization through the OpenVINO Quantization Aware Training framework. In most cases, 8-bit quantization reduces model accuracy by less than 1% while achieving 2-4x speedup and 75% model size reduction. You can experiment with different quantization levels and validate accuracy on your specific dataset before production deployment.

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.

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