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An Efficient Routability-Driven Analytic Placer for Ultrascale FPGA Architectures

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Title: An Efficient Routability-Driven Analytic Placer for Ultrascale FPGA Architectures
Author: Abuowaimer, Ziad
Department: School of Engineering
Program: Engineering
Advisor: Areibi, Shawki
Abstract: Field Programmable Gate Arrays (FPGAs) continue to find increasingly wide use in commercial products as a good trade-off between CPU and ASIC, due to their flexibility and versatility. The increasing complexity and scale of modern FPGAs impose great challenges on the FPGA Computer-Aided Design (CAD) flow. However, within the FPGA CAD flow, placement remains one of the most important, time-consuming steps. In this thesis, we developed two novel routability-driven analytic placement tools for Xilinx UltraScale architectures, GPlace-pack and GPlace-flat. The former (GPlace-pack) placed third in the ISPD 2016 Routability-driven Placement Contest for FPGAs. The later (GPlace-flat) is a flat analytic placer which incorporates 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, GPlace-flat is able to achieve (on average) a 7.53%, 15.15%, and 33.50% reduction in routed wirelength, respectively, while requiring less overall runtime. Despite the superiority results that are achieved by GPlace-flat compared to the state-of-the-art placers, there is no one best flow for all benchmarks. Therefore, we present a general machine-learning framework, that seeks to address the disconnect between different stages of the FPGA CAD flow that adversely affect the quality of results of the implemented designs.
URI: http://hdl.handle.net/10214/12985
Date: 2018-04
Rights: Attribution-NonCommercial 2.5 Canada


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Attribution-NonCommercial 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NonCommercial 2.5 Canada