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

This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs. OpenPARF is implemented with the deep learning toolkit PyTorch and supports massive parallelization on GPU. The framework proposes a novel asymmetric multi-electrostatic field system to solve FPGA placement. It considers fine-grained routing resources inside configurable logic blocks (CLBs) for FPGA routing and supports large-scale irregular routing resource graphs. Experimental results on ISPD 2016 and ISPD 2017 FPGA contest benchmarks and industrial benchmarks demonstrate that OpenPARF can achieve 0.4-12.7% improvement in routed wirelength and more than $2\times$ speedup in placement. We believe that OpenPARF can pave the road for developing FPGA physical design engines and stimulate further research on related topics

FPGA Placement and Routing Using Particle Swarm Optimization

Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA\u27s fixed resources is an efficient placement and routing mechanism. This paper presents particle swarm optimization (PSO) for FPGA placement and routing. Preliminary results for the implementation of an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem