Abstract
To minimize power consumption while maximizing performance, today’s multicore processors rely on fine-grained run-time dynamic power information – both in the time domain, e.g. \(\mu s\) to ms, and space domain, e.g. core-level. The state-of-the-art for deriving such power information is mainly based on predetermined power models which use linear modeling techniques to determine the core-performance/core-power relationship. However, with multicore processors becoming ever more complex, linear modeling techniques cannot capture all possible core-performance related power states anymore. Although, artificial neural networks (ANN) have been proposed for coarse-grained power modeling of servers with time resolutions in the range of seconds, no work has yet investigated fine-grained ANN-based power modeling. In this paper, we explore feed-forward neural networks (FFNNs) for core-level power modeling with estimation rates in the range of 10 kHz. To achieve a high estimation accuracy, we determine optimized neural network architectures and train FFNNs on performance counter and power data from a complex-out-of-order processor architecture. We show that, relative power estimation error decreases on average by 7.5% compared to a state-of-the-art linear power modeling approach and decreases by 5.5% compared to a multivariate polynomial regression model. Furthermore, we propose an implementation for run-time inference of the power modeling FFNN and show that the area overhead is negligible.
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This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 146371743 - TRR 89 “Invasive Computing”.
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Sagi, M., Vu Doan, N.A., Fasfous, N., Wild, T., Herkersdorf, A. (2020). Fine-Grained Power Modeling of Multicore Processors Using FFNNs. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2020. Lecture Notes in Computer Science(), vol 12471. Springer, Cham. https://doi.org/10.1007/978-3-030-60939-9_13
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