Evaluating a Machine Learning-based Approach for Cache Configuration

被引:0
作者
Ribeiro, Lucas [1 ]
Jacobi, Ricardo [1 ]
Junior, Francisco [1 ]
da Silva, Jones Yudi [1 ]
Silva, Ivan Saraiva [2 ]
机构
[1] Univ Brasilia, Brasilia, DF, Brazil
[2] Univ Fed Piaui, Teresina, Piaui, Brazil
来源
2022 IEEE 13TH LATIN AMERICAN SYMPOSIUM ON CIRCUITS AND SYSTEMS (LASCAS) | 2022年
关键词
Cache Memory Design; Dynamic Cache Reconfiguration; Machine Learning; Classification;
D O I
10.1109/LASCAS53948.2022.9789040
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As the systems perform progressively complex tasks, the search for energy efficiency in computational systems is constantly increasing. The cache memory has a fundamental role in this issue. Through dynamic cache reconfiguration techniques, it is possible to obtain an optimal cache configuration that minimizes the impacts of energy losses. To achieve this goal, a precise selection of cache parameters plays a fundamental role. In this work, a machine learning-based approach is evaluated to predict the optimal cache configuration for different applications considering their dynamic instructions and a variety of cache parameters, followed by experiments showing that using a smaller set of application instructions it is already possible to obtain good classification results from the proposed model. The results show that the model obtains an accuracy of 96.19% using the complete set of RISC-V instructions and 96.33% accuracy using the memory instructions set, a more concise set of instructions that directly affect the cache power model, besides decreasing the model complexity.
引用
收藏
页码:180 / 183
页数:4
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