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Authors
Advisor(s)
Abstract(s)
Modern multicore processors for the embedded market
are often heterogeneous in nature. One feature often available
are multiple sleep states with varying transition cost for entering
and leaving said sleep states. This research effort explores the
energy efficient task-mapping on such a heterogeneous multicore
platform to reduce overall energy consumption of the system.
This is performed in the context of a partitioned scheduling
approach and a very realistic power model, which improves
over some of the simplifying assumptions often made in the
state-of-the-art. The developed heuristic consists of two phases,
in the first phase, tasks are allocated to minimise their active
energy consumption, while the second phase trades off a higher
active energy consumption for an increased ability to exploit
savings through more efficient sleep states. Extensive simulations
demonstrate the effectiveness of the approach.