OpenPARF: An Open-source Placement and Routing Framework for Large-scale Heterogeneous FPGAs with Deep Learning Toolkit

An Open-source Placement And Routing Framework (OpenPARF) for large-scale FPGA physical design is proposed in this paper. OpenPARF is implemented with of deep learning toolkit PyTorch and supports GPU massive parallel acceleration. For placement, the framework incorporates a novel asymmetric multi-electrostatic filed system to model the FPGA placement problem. For routing, OpenPARF integrates finer-grained internal routing of FPGA Configurable Logic Blocks (CLBs) in the routing model and supports routing on large-scale irregular routing resource graph. This study can significantly improve the FPGA routing algorithm's efficiency and effectiveness. Experimental results on ISPD 2016 and ISPD 2017 FPGA conest benchmarks and industrial-level FPGA benchmarks demonstrate that OpenPARF can achieve 0.4%similar to 12.7% improvement in routed wirelength and more than two times speedup in placement.