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

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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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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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

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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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References Weight

Web of Science® Citations for all references: 7,718 TCR
SCOPUS® Citations for all references: 11,266 TCR

Web of Science® Average Citations per reference: 297 ACR
SCOPUS® Average Citations per reference: 433 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 2018-10-23 09:14 in 172 seconds.

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

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