Concurrent topology and routing optimization in automotive network integration

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Concurrent topology and routing optimization in automotive network integration

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Annual ACM IEEE Design Automation Conference archive
Proceedings of the 45th annual Design Automation Conference table of contents

Anaheim, California

SESSION: Design methods for on-chip communication table of contents

Pages 626-629

Year of Publication: 2008

ISBN ~ ISSN:0738-100X , 978-1-60558-115-6

Authors

Martin Lukasiewycz	University of Erlangen-Nuremberg, Germany
Michael Glaß	University of Erlangen-Nuremberg, Germany
Christian Haubelt	University of Erlangen-Nuremberg, Germany
Jürgen Teich	University of Erlangen-Nuremberg, Germany
Richard Regler	EE-81, Audi AG, Ingolstadt, Germany
Bardo Lang	EE-81, Audi AG, Ingolstadt, Germany

Sponsors

SIGDA: ACM Special Interest Group on Design Automation
: IEEE/CASS/CANDE/CEDA
: The EDA Consortium

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ACM New York, NY, USA

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Downloads (6 Weeks): 6, Downloads (12 Months): 51, Citation Count: 0

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ABSTRACT

In this paper, a novel automatic approach for the concurrent topology and routing optimization that achieves a high quality network layout is proposed. This optimization is based on a specialized binary Integer Linear Program (ILP) in combination with a Multi-Objective Evolutionary Algorithm (MOEA). The ILP is formulated such that each solution represents a topology and routing that fulfills all requirements and demands of the network. Thus, in an iterative process, this ILP is solved to obtain feasible networks whereas the MOEA is used for the optimization of multiple even non-linear objectives and ensures a fast convergence towards the optimal solutions. Additionally, a domain specific preprocessing algorithm for the ILP is presented that decreases the problem complexity and, thus, allows to optimize large and complex networks efficiently. The experimental results validate the performance of this methodology on two state-of-the-art prototype automotive networks.

REFERENCES

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	1	Nilanjan Banerjee , Rajeev Kumar, Multiobjective network design for realistic traffic models, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England [doi>10.1145/1276958.1277341]
	2	CAN. Controller Area Network. http://www.can.bosch.com/.
	3	Donald Chai , Andreas Kuehlmann, A fast pseudo-boolean constraint solver, Proceedings of the 40th conference on Design automation, June 02-06, 2003, Anaheim, CA, USA [doi>10.1145/775832.776041]
	4	Abhijit Davare , Qi Zhu , Marco Di Natale , Claudio Pinello , Sri Kanajan , Alberto Sangiovanni-Vincentelli, Period optimization for hard real-time distributed automotive systems, Proceedings of the 44th annual conference on Design automation, June 04-08, 2007, San Diego, California [doi>10.1145/1278480.1278553]
	5	M. Gruber and G. Raindl. A new 0--1 ILP approach for the bounded diameter minimum spanning tree problem. In Proc. of INOC '05, volume 1, pages 178--185, Lisbon, Portugal, 2005.
	6	J. D. Knowles and D. W. Corne. A comparison of encodings and algorithms for multiobjective spanning tree problems. In Proc. of CEC '01, pages 544--551, 2001.
	7	A. Konak and A. Smith. Capacitated network design considering survivability: An evolutionary approach. In Journal of Engineering Optimization, volume 36(2), pages 189--205, 2004.
	8	R. Kumar, P. K. Singh, and P. P. Chakrabarti. Multiobjective EA approach for improved quality of solutions for spanning tree problem. In Proc. of EMO '05, pages 811--825, 2005.
	9	M. Lukasiewycz, M. Glaß, C. Haubelt, and J. Teich. Sat-decoding in evolutionary algorithms for discrete constrained optimization problems. In Proc. of the CEC '07, pages 935--942, 2007.
	10	Martin Lukasiewycz , Michael Glaß , Christian Haubelt , Jürgen Teich, Efficient symbolic multi-objective design space exploration, Proceedings of the 2008 conference on Asia and South Pacific design automation, January 21-24, 2008, Seoul, Korea
	11	Opt4J. Optimization framework for java. http://www.opt4j.org/.
	12	Traian Pop , Petru Eles , Zebo Peng, Holistic scheduling and analysis of mixed time/event-triggered distributed embedded systems, Proceedings of the tenth international symposium on Hardware/software codesign, May 06-08, 2002, Estes Park, Colorado [doi>10.1145/774789.774828]
	13	A directed cycle-based column-and-cut generation method for capacitated survivable network design, Networks, v.43 n.4, p.201-211, July 2004 [doi>10.1002/net.v43:4]
	14	Kai Richter , Rolf Ernst, How OEMs and suppliers can face the network integration challenges, Proceedings of the conference on Design, automation and test in Europe: Designers' forum, March 06-10, 2006, Munich, Germany
	15	T. Streichert, C. Haubelt, and J. Teich. Multi-Objective Topology Optimization for Networked Embedded Systems. In Proc. of IC-SAMOS '06, pages 93--98, 2006.
	16	Dirk Ziegenbein , Jan Uerpmann , Rolf Ernst, Dynamic response time optimization for SDF graphs, Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design, November 05-09, 2000, San Jose, California
	17	E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In Proc. of EUROGEN '01, pages 95--100, 2002.