Trading-off Accuracy vs Energy in Multicore Processors via Evolutionary Algorithms Combining Loop Perforation and Static Analysis-Based Scheduling

This work addresses the problem of energy efficient scheduling and allocation of tasks in multicore environments, where the tasks can permit certain loss in accuracy of either final or intermediate results, while still providing proper functionality. Loss in accuracy is usually obtained with techniques that decrease computational load, which can result in significant energy savings. To this end, in this work we use the loop perforation technique that transforms loops to execute a subset of their iterations, and integrate it in our existing optimisation tool for energy efficient scheduling in multicore environments based on evolutionary algorithms and static analysis for estimating energy consumption of different schedules. The approach is designed for multicore XMOS chips, but it can be adapted to any multicore environment with slight changes. The experiments conducted on a case study in different scenarios show that our new scheduler enhanced with loop perforation improves the previous one, achieving significant energy savings (31 % on average) for acceptable levels of accuracy loss.