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.

翻译：本文提出OpenPARF，一个面向大规模FPGA设计的开源布局布线框架。OpenPARF基于深度学习工具包PyTorch实现，支持在GPU上进行大规模并行化处理。该框架提出了一种新颖的非对称多静电场系统以解决FPGA布局问题。在布线过程中，框架考虑了可配置逻辑块（CLB）内部的细粒度布线资源，并能支持大规模不规则布线资源图。在ISPD 2016和ISPD 2017 FPGA竞赛基准测试及工业基准测试上的实验结果表明，OpenPARF在布线线长上可实现0.4%-12.7%的改进，并在布局速度上获得超过2倍的加速。我们相信，OpenPARF可为开发FPGA物理设计引擎铺平道路，并促进相关领域的进一步研究。