UC Irvine

This paper describes a technique for performing mapping and scheduling of tasks belonging to an executable application into a NoC-based MPSoC, starting from its UML specification. A toolchain is used in order to transform the high-level UML specification into a middle-level representation, which takes the form of an annotated task graph. Such an input task graph is used by an optimization engine for the sake of carrying out the design space exploration. The optimization engine relies on a Population-based Incremental Learning (PBIL) algorithm for performing mapping and scheduling of tasks into the NoC. The PBIL algorithm is also proposed for dynamic mapping of tasks in order to deal with failure events at runtime. Simulation results are promising and exhibit a good performance of the proposed solution when problem size is increased. © 2013 Elsevier B.V. All rights reserved.