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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  1/2014 - 3

Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement Policy

CHUNG, Y. M. See more information about CHUNG, Y. M. on SCOPUS See more information about CHUNG, Y. M. on IEEExplore See more information about CHUNG, Y. M. on Web of Science, HALIM, Z. A. See more information about HALIM, Z. A. on SCOPUS See more information about HALIM, Z. A. on SCOPUS See more information about HALIM, Z. A. on Web of Science
 
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Download PDF pdficon (723 KB) | Citation | Downloads: 397 | Views: 1,763

Author keywords
cache memory, fuzzy neural networks, Takagi-Sugeno model, replacement policy, supervised learning

References keywords
cache(12), fuzzy(10), systems(8), replacement(7), system(6), policies(5), performance(5), adaptive(5), neuro(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 15 - 24
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01003
Web of Science Accession Number: 000332062300003
SCOPUS ID: 84894609777

Abstract
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To date, no cache memory replacement policy that can perform efficiently for all types of workloads is yet available. Replacement policies used in level 1 cache memory may not be suitable in level 2. In this study, we focused on developing an adaptive neuro-fuzzy inference system (ANFIS) as a replacement policy for improving level 2 cache performance in terms of miss ratio. The recency and frequency of referenced blocks were used as input data for ANFIS to make decisions on replacement. MATLAB was employed as a training tool to obtain the trained ANFIS model. The trained ANFIS model was implemented on SimpleScalar. Simulations on SimpleScalar showed that the miss ratio improved by as high as 99.95419% and 99.95419% for instruction level 2 cache, and up to 98.04699% and 98.03467% for data level 2 cache compared with least recently used and least frequently used, respectively.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


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References Weight

Web of Science® Citations for all references: 5,881 TCR
SCOPUS® Citations for all references: 10,173 TCR

Web of Science® Average Citations per reference: 226 ACR
SCOPUS® Average Citations per reference: 391 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-05 18:17 in 134 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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