adaptive neuro-fuzzy inference system as cache memory replacement policy
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ID: 196290
2014
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Abstract
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.
| Reference Key |
m.2014advancesadaptive
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|---|---|
| Authors | ;CHUNG, Y. M.;HALIM, Z. A. |
| Journal | JMIR mHealth and uHealth |
| Year | 2014 |
| DOI |
10.4316/AECE.2014.01003
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| URL | |
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