Energy-aware task scheduling for streaming applications on NoC-based MPSoCs

Streaming applications are being extensively run on portable embedded systems, which are battery -operated and with limited memory. Thus, minimizing the total energy consumption of such a system is important. We investigate the problem of offline scheduling for streaming applications composed of non-preemptible periodic dependent tasks on homogeneous Network -on -Chip (NoC)-based Multiprocessor System -on -Chip (MPSoCs) such that their total energy consumption is minimized under memory constraints. We propose a novel unified approach that integrates task -level software pipelining with Dynamic Voltage and Frequency Scaling (DVFS) to solve the problem. Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor -tree -consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intraperiod dependencies into inter -period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non -Linear Programming (NLP)-based algorithm and an Integer -Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. Using a set of real and synthetic benchmarks, we have implemented and compared our unified approach with two state-of-the-art approaches, RDAG+GeneS (Wang et al., 2011) , and JCCTS (Wang et al., 2013a). Experimental results show that our approach's maximum, average, and minimum improvements over RDAG+GeneS (Wang et al., 2011) are 31.72%, 14.05%, and 7.00%, respectively. Our approach's maximum, average, and minimum improvement over JCCTS (Wang et al., 2013a) are 35.58%, 17.04%, and 8.21%, respectively.