Designing with Versal™ AI Engine


4 days - 28 hours


  • After completing this training, you will have the necessary skills to:
    • 1 - Describe the Versal™ architecture and the complete application acceleration workflow with the Vitis™ tool.
    • 2 - Describe the architecture and the memory access structure of the AI Engine
    • 3 - Program a single AI Engine kernel using the Vitis IDE tool
    • 4 - Program multiple AI Engine kernels using Adaptive Data Flow (ADF) graphs
    • 5 - Describe the data movement between the PS, PL, and AI Engines, and the advanced graph input specifications
    • 6 - Debug an application using the simulation debugging methodology and event traces
    • 7 - Utilize various AI Engine kernel optimization techniques
    • 8 - Implement an AI Engine kernel using intrinsics for a symmetric FIR.
    • 9 - Utilize the AI Engine DSP library for faster development


  • Comfort with the C/C++ programming language
  • Software development flow
  • Vitis software for application acceleration development flow

Concerned public

  • Technicians and Engineers in Digital Electronics
  • All our training courses are given at a distance and are accessible to people with reduced mobility.
  • Our partner AGEFIPH accompanies us to implement the necessary adaptations related to your disability.
              • agefiph


  • Release date: 02/08/2023


Objective 1

  • Overview of Versal ACAP Architecture {Lecture}
  • System design flow {Lecture, Lab}

Objective 2

  • Introduction to the Versal AI Engine Architecture {Lecture}
  • Scalar and Vector Data Types {Lecture}
  • Versal AI Engine Memory and Data Movement {Lecture}

Objective 3

  • AI Engine APIs {Lecture, Lab}

Objective 3

  • Window Data APIs {Lecture}
  • The Programming Model: Single Kernel {Lecture, Labs}

Objective 4

  • Design Analysis : Vitis Analyzer {Lecture}
  • The Programming Model: Introduction to the Data Flow Graph {Lecture}
  • The Programming Model: Multiple Kernels Using Graphs {Lecture, Lab}

Objective 5

  • ACAP Data Communication and Streaming API {Lectures, Lab}
  • Advanced Graph Input Specifications {Lectures, Lab}

Objective 6

  • AI Engine Application Debug and Trace {Lecture}

Objective 7

  • AIE Kernel Optimization – Compiler Directives {Lecture}
  • AIE Kernel Optimization – Coding Style {Lecture, Lab}

Objective 7

  • Advanced C++ Kernel Programming {Lecture, Lab}

Objective 8

  • Floating-Point Operations {Lecture}
  • AI Engine Symmetric Filter and Non-Symmetric Implementation {Lecture}
  • AI Engine Symmetric and Asymmetric Filter Implementation {Lecture, Lab}

Objective 9

  • AI Engine DSP Library Overview {Lecture, Labs}

Teaching Methods

  • Inter-company online training :
    • Presentation by Webex by Cisco
              • Webex de Cisco
    • Provision of course material in PDF format
    • Labs on Cloud PC by RealVNC
              • REALVNC

Methods of monitoring and assessment of results

  • Attendance sheet
  • Evaluation questionnaire
  • Evaluation sheet on:
    • Technical questionnaire
    • Result of the Practical Works
    • Validation of Objectives
  • Presentation of a certificate with assessment of prior learning


  • Authorized Trainer Provider AMD : Engineer Electronics and Telecommunications ENSIL
    • Expert AMD FPGA - Language VHDL/Verilog - RTL Design
    • Expert AMD SoC & MPSoC - Language C/C++ - System Design
    • Expert DSP & AMD RFSoC – HLS - Matlab - Design DSP RF
    • Expert AMD Versal – AI Engines – Heteregenous System Architect

PC Recommended

  • Software Configuration :
  • Hardware configuration:
    • Recent computer (i5 or i7)
    • OS Linux 64-bits (Windows 10 compatible)
    • At least 16GB RAM
    • Display resolution recommended 1920x1080


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