Name
Title | ||
|---|---|---|
A Machine Learning Methodology for Cache Memory Design Based on Dynamic Instructions. |
Abstract | ||
|---|---|---|
Cache memories are an essential component of modern processors and consume a large percentage of their power consumption. Its efficacy depends heavily on the memory demands of the software. Thus, finding the optimal cache for a particular program is not a trivial task and usually involves exhaustive simulation. In this article, we propose a machine learning–based methodology that predicts the optimal cache reconfiguration for any given application, based on its dynamic instructions. Our evaluation shows that our methodology reaches 91.1% accuracy. Moreover, an additional experiment shows that only a small portion of the dynamic instructions (10%) suffices to reach 89.71% accuracy.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3376920 | ACM Transactions on Embedded Computing Systems |
Keywords | DocType | Volume |
Supervised learning,cache memory,cache memory design,classification,machine learning | Journal | 19 |
Issue | ISSN | Citations |
2 | 1539-9087 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osvaldo Navarro | 1 | 3 | 1.79 |
| Jones Yudi Mori | 2 | 20 | 4.96 |
| Javier Hoffmann | 3 | 1 | 0.36 |
| Hector Gerardo Muñoz Hernandez | 4 | 1 | 0.36 |
| Michael Hübner | 5 | 1 | 1.38 |