Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement Policy

Advances in Electrical and Computer Engineering

CHUNG, Y. M. ; HALIM, Z. A..

关键词: cache memory;    fuzzy neural networks;    Takagi-Sugeno model;    replacement policy;    supervised learning;   

DOI  :  10.4316/AECE.2014.01003

学科分类：计算机科学（综合）

来源: Universitatea "Stefan cel Mare" din Suceava

【 摘 要 】

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.

【 授权许可】

Unknown