Flexible Spatio-Temporal Energy-Efficient Runtime Management

Heterogeneous multi-core architectures, such as Arm’s big.LITTLE and DynamIQ, feature multiple core types with the same ISA but varied performance-energy characteristics. These are increasingly adopted in embedded systems as they enable dynamic application mapping, balancing performance with energy efficiency. While Hybrid Application Mapping (HAM) approaches have gained popularity in systems running dynamic workloads, most solutions yield spatial mappings and neglect application migrations in output schedules, substantially limiting the solution space. This work introduces STEM and FFEMS, two algorithms utilizing the temporal aspect with job reconfigurations to generate “flexible” spatio-temporal mappings. STEM leverages Memetic Algorithms (MAs), while FFEMS uses fast greedy heuristics. Our evaluation on two heterogeneous multi-core platform models demonstrates that the flexible structure of the spatio-temporal mappings significantly improves the schedulability. On workloads from automotive and multimedia domains, STEM finds the most energy-efficient solutions, but its large overhead makes it unsuitable for use in runtime systems. In contrast, FFEMS exhibits an outstanding balance between performance and runtime overhead: Given similar runtime overhead as MMKP-MDF, the state-of-the-art approach, FFEMS schedules up to 16% more test cases. Its “tail-switch” optimization further improves energy efficiency, though with increased overhead, which is still acceptable within runtime systems.