Deep Learning Inference with Intel® FPGAs

Week 1

This class reviews the basics of deep learning and FPGAs. Topics include:

  • Machine learning terminology and use cases
  • Basic topologies such as feed-forward networks and AlexNet
  • An overview of FPGA architecture, advantages, and uses

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Week 2

This class teaches how to make computer vision applications. Topics include:

  • The essential components of computer vision software
  • How Intel® software and hardware are used to improve applications
  • The common practices, languages, tools, and libraries used for computer vision

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Week 3

This class teaches about the Intel Distribution of OpenVINO toolkit. Topics include:

  • An overview of the Intel Distribution of OpenVINO toolkit and how to use each component for computer vision
  • How to convert and optimize a Caffe* or TensorFlow* model into the format for the inference engine
  • Why using the inference engine for FPGA accelerator speeds up vision applications

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Week 4

This class explains the Intel® FPGA Deep Learning Acceleration Suite. Topics include:

  • How the Intel Distribution of OpenVINO toolkit can map network topologies onto FPGA architecture
  • The different deep learning architectures available for FPGAs
  • How lower precision is handled in FPGA machine learning models

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Week 5

This class explains how the acceleration stack can be used for FPGAs. Topics include:

  • How to use the acceleration stack to enable FPGA clusters
  • Learning about the Open Programmable Acceleration Engine (OPAE) for application developers
  • How to set up a host application to discover an FPGA accelerator

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