Fricke F, Scharoba S, Rachuj S, Konopik A, Kluge F, Hofstetter G, Reichenbach M (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13511 LNCS
Pages Range: 235-249
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Samos, GRC
ISBN: 9783031150739
DOI: 10.1007/978-3-031-15074-6_15
Estimating execution time is a crucial task during the development of safety-critical embedded systems. Processor simulation or emulation tools on various abstraction levels offer a trade-off between accuracy and runtime. Typically, this requires detailed knowledge of the processor architecture and high manual effort to construct adequate models. In this paper, we explore how deep learning may be used as an alternative approach for building processor performance models. First, we describe how to obtain training data from recorded execution traces. Next, we evaluate various neural network architectures and hyperparameter values. The accuracy of the best network variants is finally compared to two simple baseline models and a mechanistic model based on the QEMU emulator. As an outcome of this evaluation, a model based on the Wavenet architecture is identified, which outperforms all other approaches by achieving a mean absolute percentage error of only 1.63%.
APA:
Fricke, F., Scharoba, S., Rachuj, S., Konopik, A., Kluge, F., Hofstetter, G., & Reichenbach, M. (2022). Application Runtime Estimation for AURIX Embedded MCU Using Deep Learning. In Alex Orailoglu, Marc Reichenbach, Matthias Jung (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 235-249). Samos, GRC: Springer Science and Business Media Deutschland GmbH.
MLA:
Fricke, Florian, et al. "Application Runtime Estimation for AURIX Embedded MCU Using Deep Learning." Proceedings of the 22nd International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, Samos, GRC Ed. Alex Orailoglu, Marc Reichenbach, Matthias Jung, Springer Science and Business Media Deutschland GmbH, 2022. 235-249.
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