An Objective-Flexible Clustering Algorithm for task mapping and scheduling on cluster-based NoC

An Objective-Flexible Clustering Algorithm (OFCA), which is applicable to multiple design objectives and targets high performance and low energy task mapping and scheduling on homogenous cluster-based NoC, is presented. OFCA employs a lineal clustering to group tasks into clusters, and utilizes an efficient heuristic task mapping process to allocate ready clusters onto the platform. Then a low latency pipeline-based static task scheduling stage is proposed to arrange task sequence in IP cores. Finally a best hardware resource demand for the application could be predicted for reference. OFCA can fully exploit the parallel characteristics within task graphs to minimize inter-cluster communication and limit copying tasks when clustering to reduce extra execution energy. It also controls the use of task-duplication-technique (TDT) by setting different parameters for flexible goals to make a compromise between energy and latency. Experiments show that objectives can be adjusted via different parameter ratios performing OFCA and 57% energy savings on average can be achieved compared to CM Algorithm when employed to streaming applications of 18, 36, and 40 tasks.