Abstract—Rail Route Optimization System. Symbolic encoding for resource allocation, task binding, and message routing during multi-objective design space exploration (DSE) has gained significant attention in recent years. To determine the message routing, existing symbolic approaches typically rely on an explicit encoding of routing hops which results in a huge number of required variables and/or constraints. As a result, these approaches fail in case of large network diameters and/or a huge number of messages or resources and even for smaller problems, the convergence of the involved optimization process suffers. To tackle this shortcomings, this work proposes three novel symbolic routing encoding strategies that all avoid to encode hops explicitly, but are based on an encoding of individual links or complete sender-receiver paths, but still cover the same design space. The result is a more compact problem representation with less constraints and, in particular, < Final Year Projects > less variables; the latter eliminates ineffective degrees of freedom from the search space and significantly enhances the optimization quality of a multi-objective optimization with even non-linear objectives. In an extensive test-suite, three major classes of wired networked embedded systems are considered: (a) hierarchical stars as in MPSoCs or automotive, (b) redundant backbone buses as common in rail systems or avionics, and (c) mesh-based architectures that often occur in NoC-based MPSoCs. For all three classes, the proposed approaches significantly outperform existing techniques in both scalability and optimization quality and, thus, considerably enlarge the field of application of a multi-objective DSE for the networked embedded system design.
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