Compilation and Analysis - 2022.2 English

Vitis Tutorials: AI Engine Development

Document ID
XD100
Release Date
2022-12-01
Version
2022.2 English

Navigate to the MultiKernel directory. In the Makefile three methods are defined:

  • aie

    • Compiles the graph and the kernels

  • aie_sim

    • Runs the AI Engine System C simulator

  • aie_viz

    • Runs vitis_analyzeron the output summary

Take a look at the source code (kernel and graph) to familiarize yourself with C++ instantiation of kernels. In graph.cpp the PL AI Engine connections are declared using 64-bit interfaces running at 500 MHz, allowing for maximum bandwidth on the AI Engine array AXI-Stream network.

To have the simulation running, input data must be generated. There are 2 possibilities:

  1. Just type make data

  2. Change directory to data and type GenerateStreamsGUI. The following parameters should be set for this example:

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Click Generate then Exit. The generated files PhaseIn_0_0.txtPhaseIn_7_7.txt should contain mainly 0’s, with a few 1’s and 2’s. The number of samples per stream is half of the one that is declared in the C++ code because in the C++ code this is the length of the concatenation of both input streams.

Type make all and wait for the vitis_analyzer GUI to Display. The Vitis analyzer is able to show the graph, how it has been implemented in the device, and the complete timeline of the simulation. In this specific case the graph is very simple (a single kernel) and the implementation is on a single AI Engine.

Click Graph to visualize the graph of the application:

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The 64 kernels and their 16 independent input streams are clearly visible. The top graph is for the output phases 0, 2, 4, and 6, the phases where the cascade stream goes from left to right on the physical device, and the bottom graph is for the phases 1, 3, 5, and 7 where the cascade stream goes from right to left.

Click Array to visualize where the kernel has been placed, and how it is fed from the the PL:

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In this view the cascade streams connecting neighboring AI Engines are key to the performance of this graph. With the four location constraints that were added, the placer had only one solution for the kernel placement: this square. The router had an easy job to feed all these kernels by simply using the south-north AXI-Stream. The path back to the PL from the extremities also uses only the vertical AXI-Streams.

Finally click Trace to look at how the entire simulation went through. This may be useful to track where your AI Engine stalls if performance is not as expected:

Now the output of the filter can be displayed. The input being a set of Dirac impulses, the impulse response of the filter should be recognized throughout the waveform. Navigate to Emulation-AIE/aiesimulator_output/data and look at the output_0.txt. You can see that you have two complex outputs per line which is prepended with a time stamp. ProcessAIEOutput output_*.