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


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


  • Data scientists
 35 Hours

Number of participants

Price per participant

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