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Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Unit (VPU)

Deep Neural Networks

Optimized for computer vision applications built using deep neural networks for low power, low cost, and small form factors

Power Efficiency

Ultra-low-power demands allow for integration on cameras and edge servers running on power- and size-constrained systems

Easy to Scale

Scalable analytics with minimal software changes for single- to multiple-chip solutions 

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Overview

Specialized processors designed to deliver high-performance machine vision at ultra-low power.

  • Supports up to 16 video streams per device
  • Ideal for camera and network video recorder (NVR) use cases with power, size, and cost constraints
  • Supports small memory footprint networks

Virtual Testing with Intel® Developer Cloud

Quickly prototype and develop AI applications in the cloud using the latest Intel® hardware and software tools.

Learn More

Who Needs This Product

Information and operational technologists who:

  • Are new to IoT commercial platforms and need a simple path without a steep learning curve
  • Create solutions that offload deep learning and AI workloads from the CPU or GPU to dedicated accelerator products
  • Need a quicker path to deployment

Use Cases

  • Smart cities
  • Automotive and transportation
  • Healthcare
  • Retail
  • Digital security

Specifications

Intel Vision Accelerator Design

Features

With 1 Intel Movidius VPU

With 2 Intel Movidius VPUs

With 8 Intel Movidius VPUs

VPU

1 - MA2485

2 - MA2485

8 - MA2485

Board dimensions

M.2 2230 Key E & A

22 mm x 30 mm

M-PCIe*

30 mm x 50 mm

Half-height, half-length, single-slot PCIe*

68.90 mm x 167.65 mm

VPU memory

4 GB LPDDR4 POP

4 GB LPDDR4 POP

4 GB LPDDR4 POP

Minimum system configuration

 

Intel Atom® x7 processor E3950

8 GB LPDDR4, 64 GB eMMC

USB 3.0, M.2 2230 connector

Intel Atom x7 processor E3950

8 GB LPDDR4, 64 GB eMMC

USB 3.0, M-PCIe connector

Intel® Core™ i5 processor 6500TE

8 GB RAM, 500 GB HDD

USB 3.0

2 - PCIe x4 x8 x16 connectors

Typical operating system

 

Ubuntu* 16.04 LTS 64 bit

Ubuntu 16.04 LTS 64 bit

Ubuntu 16.04 LTS 64 bit

Tools

Intel® Distribution of OpenVINO™ toolkit

Intel Distribution of OpenVINO toolkit

Intel Distribution of OpenVINO toolkit

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Supported Streams

Typically supports 1 to 16 video streams per device (depends on desired frame rate and algorithm complexity)

Efficiency

High efficiency

Precision

Supports FP16 precision networks

Customization

Hardware optimized for generic cases

Software

Intel® Distribution of OpenVINO™ Toolkit

  • Enable deep learning inference on the edge based on convolutional neural networks.
  • Support for heterogeneous execution across various accelerators—CPU, GPU, Intel® Movidius™ Myriad™ X Vision Processing Unit (VPU), and FPGA—using a common API.
  • Speed up time to market via a library of functions and preoptimized kernels.
  • Preinstalled models are included with release 5.

Overview | Training | Documentation | Forum | Get Started

Free Download

Hardware

 

IoT Developer Kits Supporting Intel® Movidius™ Vision Processing Unit (VPU)

Developer Kits Based on 8th Generation Intel® Core™ Processors

IEI* TANK AIoT Developer Kit (Intel Core Processor)

IEI TANK AIoT Developer Kit (Intel® Xeon® Processor)

Suppliers

Purchase or review documentation from the following suppliers.

Intel Vision Accelerator Design

Suppliers

With 1 Intel Movidius VPU

With 2 Intel Movidius VPUs

With 8 Intel Movidius VPUs

AAEON*

UP* AI Core X

UP AI Core XM 2280

UP AI Core XP8

ADLINK*

 

EDL-mPCIe-MA2485

 

Advantech*

VEGA-320

VEGA-330

 

IEI

Mustang-M2AE-MX1

Mustang-M2BM-MX2

Mustang-MPCIE-MX2

Mustang-V100-MX8-R10

JWIPC* 

 

AI Accelerator Card

 

NEXCOM*

 

 

AIBooster-X8

View all Show less

Documentation & Support

Additional Resources

Intel® Vision Accelerator Design with Intel® Arria® 10 FPGAs

 

Support

AAEON*

ADLINK*

Advantech*

IEI*

NEXCOM*

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