Application Runtime Estimation for AURIX Embedded MCU Using Deep Learning

Fricke F, Scharoba S, Rachuj S, Konopik A, Kluge F, Hofstetter G, Reichenbach M (2022)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Edited Volumes: Embedded Computer Systems: Architectures, Modeling, and Simulation

Pages Range: 235-249

ISBN: 9783031150739

DOI: 10.1007/978-3-031-15074-6_15

Abstract

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%.

Authors with CRIS profile

Sebastian Rachuj Lehrstuhl für Informatik 3 (Rechnerarchitektur)

Involved external institutions

Brandenburgische Technische Universität Cottbus-Senftenberg (BTU) DE Germany (DE) Elektronische Fahrwerksysteme GmbH DE Germany (DE)

How to cite

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 Embedded Computer Systems: Architectures, Modeling, and Simulation. (pp. 235-249).

MLA:

Fricke, Florian, et al. "Application Runtime Estimation for AURIX Embedded MCU Using Deep Learning." Embedded Computer Systems: Architectures, Modeling, and Simulation. 2022. 235-249.

BibTeX: Download