Abstract
Safe and responsive hard real-time systems require the Worst-Case Execution Time (WCET) to determine the schedulability of each software task. Not meeting planned deadlines could result in fatal consequences. During development, system designers have to make decisions without any insight in the WCET of the tasks. Early WCET estimates will help us to perform design space exploration of feasible hardware and thus lowering the overall development costs. This paper proposes to extend the hybrid WCET analysis with deep learning models to support early predictions. Two models are created in TensorFlow to be compatible with our COBRA framework. The framework provides datasets based on hybrid blocks to train each model. The feed-forward neural network has a high convergence rate and is able to learn a trend in the features. However, the error of the models are currently too large to predict meaningful upper bounds. To conclude, we summarise the problems we need to tackle to improve the accuracy and convergence issues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: a system for large-Scale Machine Learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation, vol. 16, pp. 265–283 (2016)
Boehm, B.W., et al.: Software Engineering Economics. Prentice-Hall PTR, Englewood Cliffs (1981)
Bonenfant, A., et al.: Early WCET prediction using machine learning. In: Reineke, J. (ed.) 17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017), vol. 57, pp. 5:1–5:9 (2017). https://doi.org/10.4230/OASIcs.WCET.2017.5
De Bock, Y., et al.: Task-set generator for schedulability analysis using the TACLeBench benchmark suite. In: Proceedings of the Embedded Operating Systems Workshop : EWiLi 2016, pp. 1–6 (2016). http://ceur-ws.org/Vol-1697/
Falk, H., et al.: TACLeBench: a benchmark collection to support worst-case execution time research. In: Proceedings of the 16th International Workshop on Worst-Case Execution Time Analysis (WCET 2016) (2016)
Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media (2017)
Gustafsson, J., et al.: Approximate worst-case execution time analysis for early stage embedded systems development, pp. 308–319. Springer, Berlin (2009)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Hall, M.A.: Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. In: Proceedings of the 17th International Conference on Machine Learning, ICML 2000, pp. 359–366. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000)
Huybrechts, T.: Hybrid approach on cache aware real-time scheduling for multi-core systems. In: Xhafa, F., Barolli, L., Amato, F. (eds.) Advances on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 759–768. Springer International Publishing, Cham (2017)
Huybrechts, T., et al.: A new hybrid approach on WCET analysis for real-time systems using machine learning. In: Brandner, F. (ed.) 18th International Workshop on Worst-Case Execution Time Analysis (WCET 2018) (2018)
Huybrechts, T., et al.: COBRA-HPA: a block generating tool to perform hybrid program analysis. Int. J. Grid Util. Comput. (in press 2018.)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)
Lokuciejewski, P., Marwedel, P.: Worst-Case Execution Time Aware Compilation Techniques for Real-time Systems. Springer, Netherlands (2011). https://doi.org/10.1007/978-90-481-9929-7
Oyamada, M.S., et al.: Accurate software performance estimation using domain classification and neural networks. In: Proceedings of the 17th Symposium on Integrated Circuits and System Design, SBCCI 2004, pp. 175–180. ACM, New York, NY, USA (2004)
Puschner, P., Koza, C.: Calculating the maximum, execution time of real-time programs. Real-Time Syst. 1(2), 159–176 (1989). https://doi.org/10.1007/BF00571421
Reineke, J.: Caches in WCET analysis. Ph.D. thesis, University of Saarlandes (2008)
Wilhelm, R., et al.: The worst-case execution-time problem - overview of methods and survey of tools. ACM Trans. Embed. Comput. Syst. 7(3), 36:1–36:53 (2008)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Huybrechts, T., Cassimon, T., Mercelis, S., Hellinckx, P. (2019). Introduction of Deep Neural Network in Hybrid WCET Analysis. In: Xhafa, F., Leu, FY., Ficco, M., Yang, CT. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-02607-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-02607-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02606-6
Online ISBN: 978-3-030-02607-3
eBook Packages: EngineeringEngineering (R0)