The impact of formulation of cost function in Task Mapping Problem on NoCs using bio-inspired based-metaheuristics

The Task Mapping Problem (TMP) of a Real-Time Application (RTA) onto a Many-/Multi-Processor System-on-a-Chip (MPSoC) with a Network-on-a-Chip (NoC) as on-chip communication architecture can be tackled as an optimization problem to improve desired design features in a static analysis setting. Examples of such features are the system’s compliance with its time requirements. A search-based bio-inspired metaheuristic such as the Genetic Algorithm (GA) can achieve task placement solutions in which all tasks are schedulable. However, the optimization processes are susceptible to the way the cost function is formulated. Additionally, solutions obtained with some of these methods do not consider some of the platform characteristics, such as the number of available virtual channels in the routers, and memory requirements in the processors, rendering their solutions unsuitable for real platforms. Therefore, we propose four objective functions to unify the end-to-end schedulability analysis that considers such platform characteristics. To demonstrate the performance of our objective functions, we conduct a series of experiments mapping the tasks of a benchmark application together with synthetic generated ones onto multiple Network-on-Chip platforms with realistic characteristics. We compare the objective functions during the optimization process using four different metaheuristics, showing that the multi-objective formulation helps the task mapper to achieve solutions that are more suitable for implemented NoC platforms. Although our study focuses on homogeneous NoC-Based MPSoCs, results have generated a better understanding of how the formulation of cost functions impacts the optimization processes for the specific TMP case, considering the Multi-Objective Optimization (MOO) context.