부산대학교 도서관

As the demand for increased performance and reduced energy consumption continues to grow, Quality of Service (QoS) adjustment approaches offer an effective way to tackle those demands. One such method, approximation, has gained popularity in recent years, facilitating faster executions as well as a smaller power consumption by providing an approximated result. The areas in which these trade-offs are acceptable are numerous, but hardware-based solutions are usually domain-specific and expensive to integrate. To tackle this issue, we take a different approach, in which approximate hardware can be used (or not) in a general purpose environment and via software decisions. That is, a Multi-Processor System-on-Chip (MPSoC) that contains Central Processing Units (CPUs) that offer approximate calculations alongside the ones that offer exact calculations. However, current task partitioning algorithms do not consider the specific capabilities or requirements of such a MPSoC. This paper introduces approximation-aware partitioning algorithms using different strategies and compares the results to the State-of-the-Art (SoA). Additionally, the resulted task partitions are executed to gauge their quality compared to the SoA. Experimental results show, that the usage of an approximate CPU and approximation-aware task partitioning leads to an increased partition success rate of 21.5%. Furthermore, the execution, i.e., scheduling of the partitioned tasks until energy starvation, achieves a 3.4% extended run-time.