Abstract
Cache memories are an important component of modern processors and consume a large percentage of the processor’s power consumption. The quality of service of this cache memories relies heavily on the memory demands of the software, what means that a certain program might benefit more from a certain cache configuration which is highly inefficient for another program. Moreover, finding the optimal cache configuration for a certain program is not a trivial task and usually, involves exhaustive simulation. In this paper, we propose a machine learning-based methodology that, given an unknown application as input, it outputs a prediction of the optimal cache reconfiguration for that application, regarding energy consumption and performance. We evaluated our methodology using a large benchmark suite, and our results show a 99.8% precision at predicting the optimal cache configuration for a program. Furthermore, further analysis of the results indicates that 85% of the mispredictions produce only up to a 10% increase in energy consumption in comparison to the optimal energy consumption.
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Acknowledgements
The authors of this paper would like to thank CONACyT (grant number 359472) and CAPES Foundation for their support.
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Navarro, O., Mori, J., Hoffmann, J., Stuckmann, F., Hübner, M. (2017). A Machine Learning Methodology for Cache Recommendation. In: Wong, S., Beck, A., Bertels, K., Carro, L. (eds) Applied Reconfigurable Computing. ARC 2017. Lecture Notes in Computer Science(), vol 10216. Springer, Cham. https://doi.org/10.1007/978-3-319-56258-2_27
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DOI: https://doi.org/10.1007/978-3-319-56258-2_27
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