Branch predictor prediction: a power-aware branch predictor for high-performance processors

A. Baniasadi, Andreas Moshovos
{"title":"Branch predictor prediction: a power-aware branch predictor for high-performance processors","authors":"A. Baniasadi, Andreas Moshovos","doi":"10.1109/ICCD.2002.1106813","DOIUrl":null,"url":null,"abstract":"We introduce branch predictor prediction (BPP) as a power-aware branch prediction technique for high performance processors. Our predictor reduces branch prediction power dissipation by selectively turning on and off two of the three tables used in the combined branch predictor BPP relies on a small buffer that stores the addresses and the sub-predictors used by the most recent branches executed. Later we refer to this buffer to decide if any of the sub-predictors and the selector could be gated without harming performance. In this paper we study power and performance trade-offs for a subset of SPEC 2k benchmarks. We show that on the average and for an 8-way processor, BPP can reduce branch prediction power dissipation by 28% and 14% compared to non-banked and banked 32k predictors respectively. This comes with a negligible impact on performance (1% max). We show that BPP always reduces power even for smaller predictors and that it offers better overall power and performance compared to simpler predictors.","PeriodicalId":164768,"journal":{"name":"Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCD.2002.1106813","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34

Abstract

We introduce branch predictor prediction (BPP) as a power-aware branch prediction technique for high performance processors. Our predictor reduces branch prediction power dissipation by selectively turning on and off two of the three tables used in the combined branch predictor BPP relies on a small buffer that stores the addresses and the sub-predictors used by the most recent branches executed. Later we refer to this buffer to decide if any of the sub-predictors and the selector could be gated without harming performance. In this paper we study power and performance trade-offs for a subset of SPEC 2k benchmarks. We show that on the average and for an 8-way processor, BPP can reduce branch prediction power dissipation by 28% and 14% compared to non-banked and banked 32k predictors respectively. This comes with a negligible impact on performance (1% max). We show that BPP always reduces power even for smaller predictors and that it offers better overall power and performance compared to simpler predictors.
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
分支预测器预测:高性能处理器的功率感知分支预测器
我们介绍了分支预测器预测(BPP)作为高性能处理器的功率感知分支预测技术。我们的预测器通过选择性地打开和关闭组合分支预测器中使用的三个表中的两个表来减少分支预测功耗。BPP依赖于一个小缓冲区,该缓冲区存储最近执行的分支使用的地址和子预测器。稍后,我们将引用该缓冲区来决定是否可以在不影响性能的情况下对子预测器和选择器进行门控。在本文中,我们研究了SPEC 2k基准测试子集的功耗和性能权衡。我们表明,平均而言,对于8路处理器,与非银行和银行32k预测器相比,BPP可以将分支预测功耗分别降低28%和14%。这对性能的影响微不足道(最多1%)。我们表明,即使对于较小的预测器,BPP也总是降低功耗,并且与更简单的预测器相比,它提供了更好的整体功耗和性能。
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
CiteScore
2.30
自引率
0.00%
发文量
0
期刊最新文献
JMA: the Java-multithreading architecture for embedded processors Legacy SystemC co-simulation of multi-processor systems-on-chip Accurate and efficient static timing analysis with crosstalk Register binding based power management for high-level synthesis of control-flow intensive behaviors On the impact of technology scaling on mixed PTL/static circuits
×
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