Computer Vision Hardware

Choose the hardware accelerator that maximizes the performance of your application for any type of processor.

Intel® Processors 

These CPUs offer the most universal option for computer vision tasks. With multiple product lines to choose from, you can find a range of price and performance options to meet your application and budget needs.

Common uses:

  • Data analytics and cloud computing
  • Energy efficient servers
  • Network video recorders

Works best for:

  • General-purpose processing, emphasizing performance and flexibility
  • Real-world testing
  • Quick-turn solutions (fast time to market)
  • Solutions that have power or heat constraints


Supported hardware:

  • 6th to 9th generation Intel® Core™ processors and Intel® Xeon® processors
  • Intel Pentium® processor N4200/5, N3350/5, or N3450/5 with Intel® HD Graphics

Supported operating systems:

  • Windows® 10 (64 bit)
  • Ubuntu* 16.04.3 LTS (64 bit)
  • CentOS* 7.4 (64 bit)
  • Yocto Project* version Poky Jethro 2.0.3 (64 bit)
  • macOS* (64 bit)

Supported Intel® Distribution of OpenVINO™ toolkit components:

Intel® Processor Graphics 

Many Intel processors contain integrated graphics, including Intel HD Graphics and Intel® UHD Graphics. The GPUs have a range of general-use and fixed-function capabilities (including Intel® Quick Sync Video) that can be used to accelerate media, inference, and general computer vision operations.

Supported hardware:

  • 6th to 9th generation Intel Core processors with Iris® Pro graphics and Intel HD Graphics
  • 6th to 9th generation Intel Xeon processor with Iris Pro graphics and Intel HD Graphics (excluding the e5 family which does not include graphics)

Supported operating systems:

  • Windows 10 (64 bit)
  • Ubuntu 16.04.3 LTS (64 bit)
  • CentOS 7.4 (64 bit)
  • Yocto Project version Poky Jethro 2.0.3 (64 bit)

Supported Intel Distribution of OpenVINO toolkit components:

Intel® FPGAs 

Gain cost savings and revenue growth from integrated circuits that retrieve and classify data in real time. Use these accelerators for AI inferencing as a low-latency solution for safer and interactive experiences that can be applied to autonomous vehicles, robotics, IoT, and data centers.

Intel® Vision Accelerator Design 


Available in a small form factor (as a PCIe* add-in card), this design enables deep learning inference at low power and low latency. It is well suited for real-time applications with limited space and power budget such as surveillance, retail, medical, and machine vision.

Common uses:

  • Achieve optimized solutions through reprogramming flexibility
  • Deliver high-performance, on-device deep learning inferences at low power and low latency
  • Execute a compact form factor that's suitable for edge systems

Works best for:

  • Industrial manufacturing
  • Digital security and surveillance
  • Retail
  • Medical


Supported hardware:

  • Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (preview)

Supported operating system:

  • Ubuntu 16.04.3 TLS (64 bit)

Supported toolkit component:


This design clusters multiple Intel® Movidius™ Vision Processing Units (VPU) (1~N) on an add-on card or rack-mount module server to provide deep learning inference acceleration. This family of vision accelerator design products comes in multiple form factors to cater to a wide range of vertical use cases.

Common uses:

  • Optimization of the video analysis pipeline that includes video codec, image processing, computer vision, and neural network inference
  • Real-time, low-latency inference
  • Offload capability for high-density, deep-learning inference workloads

Works best for:

  • Data center
  • Cloud server, edge server, and edge network video recorder (NVR) server for surveillance


Supported hardware:

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

Supported operating systems:

  • Ubuntu 16.04.3 LTS (64 bit)
  • Windows® 10 Enterprise (64 bit)

Supported component:

Image Processing Units (IPU) 

The Intel Atom® processor E3900 contains a dedicated IPU that can be used to run common OpenVX kernels for print imaging.

Supported hardware:

  • Intel Atom processor E3900 series

Supported operating systems:

  • Yocto Project* Poky Jethro v2.0.3 (64 bit)

Supported Intel Distribution of OpenVINO toolkit component:

  • OpenVX

Deep Learning for Computer Vision

Accelerate and deploy convolutional neural networks (CNN) on Intel® platforms with the Deep Learning Deployment Toolkit included in this toolkit.