中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Evaluation and choice of various branch predictors for low-power embedded processor

文献类型:期刊论文

作者Fan, DR; Yang, HB; Gao, GR; Zhao, RC
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2003-11-01
卷号18期号:6页码:833-838
关键词branch predictor embedded processor
ISSN号1000-9000
英文摘要Power is an important design constraint in embedded computing systems. To meet the power constraint, microarchitecture and hardware designed to achieve high performance need to be revisited, from both performance and power angles. This paper studies one of them: branch predictor. As well known, branch prediction is critical to exploit instruction level parallelism effectively, but may incur additional power consumption due to the hardware resource dedicated for branch prediction and the extra power consumed on mispredicted branches. This paper explores the design space of branch prediction mechanisms and tries to find the most beneficial one to realize low-power embedded processor. The sample processor studied is Godson-like processor, which is a dual-issue, out-of-order processor with deep pipeline, supporting MIPS instruction set.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000187161600018
出版者SCIENCE PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/13637]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Fan, DR
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
2.Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA
推荐引用方式
GB/T 7714
Fan, DR,Yang, HB,Gao, GR,et al. Evaluation and choice of various branch predictors for low-power embedded processor[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2003,18(6):833-838.
APA Fan, DR,Yang, HB,Gao, GR,&Zhao, RC.(2003).Evaluation and choice of various branch predictors for low-power embedded processor.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,18(6),833-838.
MLA Fan, DR,et al."Evaluation and choice of various branch predictors for low-power embedded processor".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 18.6(2003):833-838.

入库方式: OAI收割

来源:计算技术研究所

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