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JCR Impact Factor: 0.800
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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: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

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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: 948 | Views: 4,055

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.


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Cited-By CrossRef

[1] An improved adaptive neuro-fuzzy inference system as cache memory replacement policy, Chung, Yee Ming, Halim, Zaini Abdul, 2016 IEEE Industrial Electronics and Applications Conference (IEACon), ISBN 978-1-5090-0925-1, 2016.
Digital Object Identifier: 10.1109/IEACON.2016.8067400
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Faculty of Electrical Engineering and Computer Science
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