high performance computing on graphics processing units: hgpu.org

We present a framework for developing compute graph-based applications targeting the AI Engine (AIE) array of AMD Versal SoCs. This framework enables users to embed AIE-based dataflow graph prototypes directly within existing C++ applications and automatically transform them into deployable AIE graph projects. It thereby eliminates the need to manually separate host and accelerator codebases, as required by the standard AMD Vitis workflow. The framework comprises two core components: (1) a compute graph simulation library that can be linked into existing C++ programs, and (2) a Clang-based source-to-source translator that extracts simulatordefined graphs and prepares them for compilation with AMD’s AIE toolchain. We evaluate our framework using AMD’s official example graphs and show that our generated AIE code achieves performance comparable to hand-optimized Vitis implementations. Additionally, we demonstrate how C++ compile-time code execution can be leveraged to simplify the implementation of source-to-source translation and static source analysis.