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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, few works have 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\u00a0kHz. To achieve a high estimation accuracy while minimizing run-time overhead, we propose a multi-objective-optimization of the neural architecture using NSGA-II with the FFNNs being trained on performance counter and power data from a complex-out-of-order processor architecture. We show that relative power estimation error for the highest accuracy FFNN 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. For the FFNNs optimized for both accuracy and overhead, the average error decreases between 4.1% and 6.7% compared to linear modeling while offering significantly lower overhead compared to the highest accuracy FFNN. Furthermore, we propose a micro-controller-based and an accelerator-based implementation for run-time inference of the power modeling FFNN and show that the area overhead is negligible.<\/jats:p>","DOI":"10.1007\/s10766-022-00730-9","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T06:14:26Z","timestamp":1648534466000},"page":"243-266","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fine-Grained Power Modeling of Multicore Processors Using FFNNs"],"prefix":"10.1007","volume":"50","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7380-3793","authenticated-orcid":false,"given":"Mark","family":"Sagi","sequence":"first","affiliation":[]},{"given":"Nguyen Anh","family":"Vu Doan","sequence":"additional","affiliation":[]},{"given":"Nael","family":"Fasfous","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Wild","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Herkersdorf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"730_CR1","unstructured":"ARM Limited: Cortex-M0 technical reference manual. 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