Developing the AI Engine Graph and Kernels - 2020.2 English

Versal ACAP Design Guide (UG1273)

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2020.2 English

An AI Engine program comprises a dataflow graph specification written in C++, which consists of nodes and edges. Nodes represent compute kernel functions, and edges represent data connections. Kernels in the application can be compiled to run on the AI Engines or in the PL region of the device. The AI Engine graph specification is compiled using the aiecompiler and executed with the aiesimulator.

Xilinx recommends gradually refining and testing the graph, slowly progressing from scalar to vectorized operations. Using scalars, you can target AI Engine tiles without having to code with intrinsics right away. This allows you to set up your system (e.g., build scripts, functional correctness, etc.) without having to do low-level AI Engine coding.

The graph is tested with a user-written test bench that drives and manages the graph using the graph APIs. The test bench and graph APIs serve as the foundation for the development of PS firmware in later steps. There are multiple methods for getting data into and out of a graph. Run-time parameters (RTPs) are programmable registers for scalar values. GMIOs provide a direct connection from the AI Engine to global memory. Streaming connections provide a direct connection between AI Engine kernels and PL kernels modeled with PLIOs in the simulation. At this stage in development, file I/O is often the simplest and most effective way to get data into and out of your graph.

Meeting performance in the aiesimulator at this early stage in the design is not a benchmark of the final system performance, because performance data is idealistic at this point. The impact of going out of or into the graph through the PLIOs is difficult to model, which limits the ability to accurately estimate performance.