Exploration of Distributed Automotive Systems Using Compositional Timing Analysis
This chapter presents a design space exploration method for mixed event-triggered and time-triggered real-time systems in the automotive domain. A design space exploration model is used that is capable of modeling and optimizing state-of-the-art automotive systems including the resource allocation, task distribution, message routing, and scheduling. The optimization is based on a heuristic approach that iteratively improves the system design. Within this iterative optimization it is necessary to analyze each system design where one of the major design objectives that needs to be evaluated is the timing behavior. Since timing analysis is a very complex design task with high computational demands, it might become a bottleneck within the design space exploration. As a remedy, a clustering strategy is presented that is capable of reducing the complexity and minimizing the runtime of the timing analysis. A case study gives evidence of the efficiency of the proposed approach.
This work was financially supported in part by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme.
- 1.Richter, K., Ziegenbein, D., Jersak, M., Ernst, R.: Model composition for scheduling analysis in platform design. In: Proceedings of the 39th Conference on Design Automation (DAC 2002), pp. 287–292 (2002)Google Scholar
- 2.Anssi, S., Albers, K., Dörfel, M., Gérard, S.: ChronVAL/ChronSIM: a tool suite for timing analysis of automotive applications. In: Proceedings of the Conference on Embedded Real-time Software and Systems (ERTS 2012) (2012)Google Scholar
- 3.Chakraborty, S., Kunzli, S., Thiele, L.: A general framework for analysing system properties in platform-based embedded system designs. In: Proceedings of the Conference on Design, Automation and Test in Europe (DATE 2003), pp. 190–195 (2003)Google Scholar
- 5.Lukasiewycz, M., Streubühr, M., Glaß, M., Haubelt, C., Teich, J.: Combined system synthesis and communication architecture exploration for MPSoCs. In: Proceedings of the Conference on Design, Automation and Test in Europe (DATE 2009), pp. 472–477 (2009)Google Scholar
- 6.Lukasiewycz, M., Glaß, M., Haubelt, C., Teich, J.: SAT-decoding in evolutionary algorithms for discrete constrained optimization problems. In: Proceedings of CEC ’07, pp. 935–942 (2007)Google Scholar
- 8.Künzli, S., Hamann, A., Ernst, R., Thiele, L.: Combined approach to system level performance analysis of embedded systems. In: Proceedings of the 5th IEEE/ACM International Conference on Hardware/Software Codesign and System, Synthesis (CODES+ISSS 2007), pp. 63–68 (2007)Google Scholar
- 9.Schioler, H., Jessen, J., Nielsen, J.D., Larsen, K.G.: Network calculus for real time analysis of embedded systems with cyclic task dependencies. In: Proceedings of the 20th International Conference on Computers and Their Applications (CATA 2005), pp. 326–332 (2005)Google Scholar
- 10.Jonsson, B., Perathoner, S., Thiele, L., Yi, W.: Cyclic dependencies in modular performance analysis. In: Proceedings of the 8th ACM International Conference on Embedded software (EMSOFT 2008), pp. 179–188 (2008)Google Scholar
- 11.Aho, A.V., Hopcroft, J.E.: Ullman. Data Structures and Algorithms. Addison-Wesley, J.D. (1983)Google Scholar
- 13.Sedgewick, R.: Algorithms in C, Part 5: Graph Algorithms. Addison-Wesley (2002)Google Scholar
- 16.Lampka, K., Perathoner, S., Thiele, L.: Analytic real-time analysis and timed automata: a hybrid method for analyzing embedded real-time systems. In: Proceedings of the 9th ACM International Conference on Embedded software (EMSOFT 2009), pp. 107–116 (2009)Google Scholar