Pipelined data parallel task mapping/scheduling technique for MPSoC

Abstract

In this paper, we propose a multi-task mapping/scheduling technique for heterogeneous and scalable MPSoC. To utilize the large number of cores embedded in MPSoC, the proposed technique considers temporal and data parallelisms as well as task parallelism. We define a multi-task mapping/scheduling problem with all these parallelisms and propose a QEA (quantum-inspired evolutionary algorithm)-based heuristic. Compared with an ILP (Integer Linear Programming) approach, experiments with real-life examples show the feasibility and the efficiency of the proposed technique.