LAUSR

Fine-grained task models can exploit parallelism to achieve high performance for multiprocessor system-on-chip (MPSoC). However, fine-grained models face the issues of high-communication overhead and difficult scheduling decisions, and the two challenges are inter-dependent. To address the issues, this paper gives a full analysis of the fine-grained communication optimization technique and communication pipeline, from both time and topology perspectives, and proposes a static fine-grained communication-aware task scheduling (FCATS) approach, which integrates scheduling with communication pipeline for acyclic and cyclic applications based on the fine-grained Simulink model. The approach contains search-based scheduling with high-quality solutions utilizing genetic algorithm-integer linear programming (GA-ILP) and hybrid GA-heuristic scheduling with short solving time to meet different demands for users. The experimental results with both synthetic and real-life benchmarks on the 4/8/16-CPU platform demonstrate the efficiency of the approach on performance improvements compared to previous works.