Developing AI Inference Solutions with the Vitis AI Platform

This course describes how to use the Vitis™ AI development platform in conjunction with DNN algorithms, models, inference and training, and frameworks on cloud and edge computing platforms.

Learn more about the Vitis Unified Software Platform from Xilinx®.

See Course Outline

2-Day Instructor-led CoursePrice USDTraining Credits
Hosted Online - $299/day$5986
In-Person Registration - $399/day$7988
Printed Course Book (mailed to you)$1001
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Scheduled Classes

No Scheduled Sessions - Contact Us to ask about setting one up!

Training Duration:

2 Days

We update our schedule regularly. Stay informed.

Who should attend:

Software and hardware developers, AI/ML engineers, data scientists, and anyone who needs to accelerate their software applications using Xilinx devices.

Skills Gained

After completing this comprehensive training, you will know how to:

  • Describe Xilinx machine learning solutions with the Vitis AI development platform and environment
  • Describe the supported frameworks, network modes, and pre-trained models for cloud and edge applications
  • Utilize DNN algorithms, models, inference and training, and frameworks on cloud and edge computing platforms
  • Use the Vitis AI quantizer and AI compiler to optimize a trained model
  • Use the architectural features of the DPU processing engine to optimize a model for an edge application
  • Identify the high-level libraries and APIs that come with the Xilinx Vitis AI Library
  • Create a custom hardware overlay based on application requirements
  • Create a custom application using a custom hardware overlay and deploy the design

Course Outline

Day 1Day 2
  • Introduction to the Vitis AI Development Environment {Lecture}
  • Overview of ML Concepts {Lecture}
  • Frameworks Supported by the Vitis AI Development Environment {Lecture}
  • Setting Up the Vitis AI Development Environment {Demo}
  • AI Optimizer {Lecture}
  • AI Quantizer and AI Compiler {Lecture, Lab}
  • AI Profiler and AI Debugger {Lecture}
  • Introduction to the Deep Learning Processor Unit (DPU) {Lecture}
  • DPUCADX8G Architecture Overview {Lecture}
  • DPUCZDX8G Architecture Overview {Lecture}
  • Vitis AI Library {Lecture, Labs}
  • Creating a Custom Hardware Platform with the DPU Using the Vivado Design Suite Flow (Edge) {Lab}
  • Creating a DPU Kernel Using the Vitis Environment Flow (Edge) {Lab}
  • Creating a Vitis Embedded Acceleration Platform (Edge) {Lecture}
  • Creating a Custom Application (Edge) {Lab}

Please note: The instructor may change the content order to provide a better learning experience.

Prerequisites:

  • Basic knowledge of machine learning concepts
  • Deep learning frameworks (such as TensorFlow, Pytorch, and Caffe)
  • Comfort with the C/C++/Python programming language
  • Software development flow

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Updated 12-08-2021