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Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement PolicyCHUNG, Y. M. , HALIM, Z. A.
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cache memory, fuzzy neural networks, Takagi-Sugeno model, replacement policy, supervised learning
cache(12), fuzzy(10), systems(8), replacement(7), system(6), policies(5), performance(5), adaptive(5), neuro(4)
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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
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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