Wibheda: Framework for Data Dependency-Aware Multi-Constrained Hardware-Software Partitioning in FPGA-Based SoCs for IoT Devices

The increasing popularity of FPGA-based system-on-chip (SoC) devices for Internet of Things (IoT) applications calls for hardware-software partitioning solutions optimized for performance under stringent area and power constraints. In this work, we propose Wibheda, a heuristic based framework for data dependency-aware multi-constrained hardware-software partitioning at fine-granularity that can be employed to partition designs for FPGA-based SoCs used in IoT. Wibheda, evaluated on 6 applications from the popular CHStone benchmark suite has been shown to find solutions with 98.7% accuracy within several milliseconds compared to several minutes or hours in an existing state-of-the-art work and an exhaustive approach respectively.