Accelerating Deep Learning with FPGA and OpenVINO Training Course


An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

By the end of this training, participants will be able to:

  • Install the OpenVINO toolkit.
  • Accelerate a computer vision application using an FPGA.
  • Execute different CNN layers on the FPGA.
  • Scale the application across multiple nodes in a Kubernetes cluster.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.


  • Python programming experience
  • Experience with pandas and scikit-learn
  • Experience with deep learning and computer vision


  • Data scientists

Course Outline


Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application

Setting up OpenVINO

Overview of OpenVINO Toolkit and its Components

Understanding Deep Learning Acceleration GPU and FPGA

Writing Software That Targets FPGA

Converting a Model Format for an Inference Engine

Mapping Network Topologies onto FPGA Architecture

Using an Acceleration Stack to Enable an FPGA Cluster

Setting up an Application to Discover an FPGA Accelerator

Deploying the Application for Real World Image Recognition


Summary and Conclusion

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