Variety-aware Routing Encoding for Efficient Design Space Exploration of Automotive Communication Networks

The introduction of sophisticated ADAS has given rise to lar ger and more complex automotive communication networks whose efficient (in effort) and optimal (in qua lity) design necessarily depends on automated network design techniques. Typically, these techniques ei ther (a) optimize communication routes based on topology-independent constraint systems that encode the i nclusion of each network component in the route of a message or (b) depend on a timeand memory-expensive enume ration of all possible transmission routes to identify the optimal route. In this paper, we propose a nov el approach which combines the advantages of these two strategies to enable an efficient exploration of th e routing search space: First, the given network is preprocessed to identify so-called proxy areasin which each pair of nodes can be connected by exactly one route. Contrary to network areas with a variety of different routing possibilities, proxy areas do not offer any room for optimization. We propose two approaches—both inte grable into existing constraint systems—which exploit the knowledge gathered on proxy areas to improve the exploration efficiency during the routing optimization process. Experimental results for two mainstream topologies of automotive networks give evidence that, compared to state-of-the-art routing optimization a pproaches, the proposed approaches (a) offer an exploration speedup of up to ×185, (b) deliver network designs of equal or higher quality, and (c) enable an automated design of significantly larger automotive system .

[1]  Martin Lukasiewycz,et al.  System simulation and optimization using reconfigurable hardware , 2014, 2014 International Symposium on Integrated Circuits (ISIC).

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Martin Lukasiewycz,et al.  Combined system synthesis and communication architecture exploration for MPSoCs , 2009, 2009 Design, Automation & Test in Europe Conference & Exhibition.

[4]  Bin Wang,et al.  Multicast routing and its QoS extension: problems, algorithms, and protocols , 2000 .

[5]  Frank Dürr,et al.  Time-sensitive Software-defined Network (TSSDN) for Real-time Applications , 2016, RTNS.

[6]  Michael Glaß,et al.  Automatic Optimization of the VLAN Partitioning in Automotive Communication Networks , 2019, ACM Trans. Design Autom. Electr. Syst..

[7]  Roman Obermaisser,et al.  From a Federated to an Integrated Automotive Architecture , 2008 .

[8]  Michael Glaß,et al.  Towards scalable symbolic routing for multi-objective networked embedded system design and optimization , 2014, 2014 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[9]  Petru Eles,et al.  Stability-aware integrated routing and scheduling for control applications in Ethernet networks , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[10]  Kai Richter,et al.  On Designing Software Architectures for Next-Generation Multi-Core ECUs , 2015 .

[11]  Christian Haubelt,et al.  Exact multi-objective design space exploration using ASPmT , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[12]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[13]  Jürgen Teich,et al.  System-Level Synthesis Using Evolutionary Algorithms , 1998, Des. Autom. Embed. Syst..

[14]  Paul Pop,et al.  Fault-tolerant topology and routing synthesis for IEEE time-sensitive networking , 2017, RTNS.

[15]  Olivier Brun,et al.  Optimal design of virtual links in AFDX networks , 2012, Real-Time Systems.

[16]  Martin Lukasiewycz,et al.  SAT-decoding in evolutionary algorithms for discrete constrained optimization problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  Daniel Le Berre,et al.  The Sat4j library, release 2.2 , 2010, J. Satisf. Boolean Model. Comput..

[18]  Dirk Timmermann,et al.  ILP-based joint routing and scheduling for time-triggered networks , 2017, RTNS.

[19]  Martin Lukasiewycz,et al.  Opt4J: a modular framework for meta-heuristic optimization , 2011, GECCO '11.

[20]  Michael Glaß,et al.  Optimizing message routing and scheduling in automotive mixed-criticality time-triggered networks , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

[21]  Paul Pop,et al.  Routing optimization of AVB streams in TSN networks , 2016, SIGBED.

[22]  Michael Glaß,et al.  Automatic Optimization of Redundant Message Routings in Automotive Networks , 2018, SCOPES.

[23]  Alberto L. Sangiovanni-Vincentelli,et al.  Embedded System Design for Automotive Applications , 2007, Computer.

[24]  Michael Glaß,et al.  DAARM: Design-time application analysis and run-time mapping for predictable execution in many-core systems , 2014, 2014 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[25]  Michael Glaß,et al.  On Search-Space Restriction for Design Space Exploration of Multi-/Many-Core Systems , 2018, MBMV.