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

被引：0

作者：

Mai J. ^{[1
,2
]}

Wang J. ^{[1
,2
]}

Di Z. ^{[3
]}

Lin Y. ^{[2
]}

机构：

[1] School of Computer Science, Peking University, Beijing

[2] School of Integrated Circuits, Peking University, Beijing

[3] School of Information and Science and Technology, Southwest Jiaotong University, Chengdu

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2023年 / 45卷 / 09期

关键词：

FPGA; Integrated circuit design and design automation; Machine learning; Physical design; Placement and douting;

D O I：

10.11999/JEIT230387

中图分类号：

学科分类号：

摘要：

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%～12.7% improvement in routed wirelength and more than two times speedup in placement. © 2023 Science Press. All rights reserved.

引用

页码：3118 / 3131

页数：13

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