Partitioning and Dynamic Mapping Evaluation for Energy Consumption Minimization on NoC-Based MPSoC

Software complexity has increased considerably over recent years, needing special target architectures as NoC-based MPSoCs to fulfill the heavy storage, communication and computation requirements. The design of these systems requires efficient methodologies aggregating partitioning and mapping. In these sense, this paper explores partitioning and mapping influence on energy consumption of homogeneous NoC-Based MPSoC. In addition, it compares two strategies to achieve efficient dynamic mappings: one that map tasks directly onto processors and another one that applies a previous static task-partitioning and uses this information to choose the dynamic task mapping. Experiments with various synthetic and four embedded applications show the efficiency of the second strategy that minimizes an average of 23.5% on energy consumption.