A Compiler-Aware Framework for Partitioned Neural Network Inference on FPGA DPUs
This work analyzes the Vitis AI compiler and proposes an XIR-level splitting framework that generates independently compilable .xmodel fragments while preserving the context required for DPU mapping, and restores correct DPU mapping by addressing boundary-context loss and incomplete dependency collection.