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

Jing Mai; Jiarui Wang; Zhixiong Di; Guojie Luo; Yun Liang; Yibo Lin

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arXiv · 2306.16665

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

Jing Mai·Jiarui Wang·Zhixiong Di·Guojie Luo·Yun Liang·Yibo Lin

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Abstract

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.

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BibTeX RIS

Jing Mai, Jiarui Wang, Zhixiong Di, Guojie Luo, Yun Liang, Yibo Lin. 2023-06-29. OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit. https://arxiv.org/abs/2306.16665

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