BoxPlacer: Force Directed-Based Timing-Driven Placement for Large-Scale FPGAs: (Abstract Only)

Minghua Shen, Jiaxi Zhang, Nong Xiao, Guojie Luo
{"title":"BoxPlacer: Force Directed-Based Timing-Driven Placement for Large-Scale FPGAs: (Abstract Only)","authors":"Minghua Shen, Jiaxi Zhang, Nong Xiao, Guojie Luo","doi":"10.1145/3174243.3174977","DOIUrl":null,"url":null,"abstract":"Placement is probably the most critical process in the FPGA design flow. The demand for high performance continues to increase, but existing placers are still faced with numerous challenges including very long runtime, poor scalability, and restricted space exploration. In this paper we propose a novel timing-driven placement algorithm called BoxPlacer, which is supported by the force directed concept. BoxPlacer firstly uses a simple policy to create the initial box for placement. Then a force-directed iterative scheme is used to reduce the box size and determine the global placement. At last, the same concept is employed to eliminate the overlaps between reduced boxes to ensure the legalization in detailed placement. Notice that timing is always used to drive the placement in BoxPlacer. We demonstrate the effectiveness of our BoxPlacer by comparing the experimental results with that produced by the academic simulated annealing-based placer. Notably, our BoxPlacer achieves on average about 8x runtime advantage with 9% smaller critical path delay and 6% shorter wirelength.","PeriodicalId":164936,"journal":{"name":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","volume":"344 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3174243.3174977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Placement is probably the most critical process in the FPGA design flow. The demand for high performance continues to increase, but existing placers are still faced with numerous challenges including very long runtime, poor scalability, and restricted space exploration. In this paper we propose a novel timing-driven placement algorithm called BoxPlacer, which is supported by the force directed concept. BoxPlacer firstly uses a simple policy to create the initial box for placement. Then a force-directed iterative scheme is used to reduce the box size and determine the global placement. At last, the same concept is employed to eliminate the overlaps between reduced boxes to ensure the legalization in detailed placement. Notice that timing is always used to drive the placement in BoxPlacer. We demonstrate the effectiveness of our BoxPlacer by comparing the experimental results with that produced by the academic simulated annealing-based placer. Notably, our BoxPlacer achieves on average about 8x runtime advantage with 9% smaller critical path delay and 6% shorter wirelength.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
BoxPlacer:大规模fpga的基于力定向的时序驱动放置(仅摘要)
放置可能是FPGA设计流程中最关键的过程。对高性能的需求不断增加,但现有的放置器仍然面临着许多挑战,包括运行时间过长、可扩展性差和空间探索受限。在本文中,我们提出了一种新的时间驱动的放置算法,称为BoxPlacer,它由力导向概念支持。BoxPlacer首先使用一个简单的策略来创建用于放置的初始框。然后采用一种力导向迭代方案来减小箱体尺寸并确定全局位置。最后,采用相同的概念,消除了减少框之间的重叠,保证了详细放置的法制化。注意,时间总是用于驱动BoxPlacer中的位置。通过将实验结果与学术模拟退火砂矿机的实验结果进行比较,证明了BoxPlacer的有效性。值得注意的是,我们的BoxPlacer实现了平均约8倍的运行时优势,减少了9%的关键路径延迟,缩短了6%的无线长度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Architecture and Circuit Design of an All-Spintronic FPGA Session details: Session 6: High Level Synthesis 2 A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks: (Abstract Only) Software/Hardware Co-design for Multichannel Scheduling in IEEE 802.11p MLME: (Abstract Only) Session details: Special Session: Deep Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1