GPlace3.0

Author:

Abuowaimer Ziad1,Maarouf Dani1,Martin Timothy1,Foxcroft Jeremy1,Gréwal Gary1,Areibi Shawki1,Vannelli Anthony1

Affiliation:

1. University of Guelph, Canada

Abstract

Optimizing for routability during FPGA placement is becoming increasingly important, as failure to spread and resolve congestion hotspots throughout the chip, especially in the case of large designs, may result in placements that either cannot be routed or that require the router to work excessively hard to obtain success. In this article, we introduce a new, analytic routability-aware placement algorithm for Xilinx UltraScale FPGA architectures. The proposed algorithm, called GPlace3.0, seeks to optimize both wirelength and routability. Our work contains several unique features including a novel window-based procedure for satisfying legality constraints in lieu of packing, an accurate congestion estimation method based on modifications to the pathfinder global router, and a novel detailed placement algorithm that optimizes both wirelength and external pin count. Experimental results show that compared to the top three winners at the recent ISPD’16 FPGA placement contest, GPlace3.0 is able to achieve (on average) a 7.53%, 15.15%, and 33.50% reduction in routed wirelength, respectively, while requiring less overall runtime. As well, an additional 360 benchmarks were provided directly from Xilinx Inc. These benchmarks were used to compare GPlace3.0 to the most recently improved versions of the first- and second-place contest winners. Subsequent experimental results show that GPlace3.0 is able to outperform the improved placers in a variety of areas including number of best solutions found, fewest number of benchmarks that cannot be routed, runtime required to perform placement, and runtime required to perform routing.

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Cited by 36 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Multielectrostatic FPGA Placement Considering SLICEL–SLICEM Heterogeneity, Clock Feasibility, and Timing Optimization;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-02

3. An Adaptive Analytical FPGA Placement flow based on Reinforcement Learning;2023 International Conference on Microelectronics (ICM);2023-12-17

4. Respect the Difference: Reinforcement Learning for Heterogeneous FPGA Placement;2023 International Conference on Field Programmable Technology (ICFPT);2023-12-12

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