Domain-Adaptive Soft Real-Time Hybrid Application Mapping for MPSoCs

The mapping of soft real-time applications onto heterogeneous MPSoC architectures can have a high influence on execution properties like energy consumption or the number of deadline violations. In recent years, scenario-aware hybrid application mapping (HAM) has turned out as the state-of-the-art mapping method for input-dependent applications whose execution characteristics are in strong dependence on the input that shall be processed. In this work, we propose an extension of scenario-aware HAM that is capable of transferring its mapping strategy learned from a labeled source data domain using supervised learning into an unlabeled target domain that exhibits a shift in its data distribution. Our domain-adaptive HAM employs a run-time manager (RTM) that performs mapping selection and reconfiguration at run time based on general domain-invariant knowledge learned at design time that is valid for both the source and target domain. Evaluation based on two input-dependent applications and two MPSoC architectures demonstrates that our domain-adaptive HAM consistently outperforms state-of-the-art mapping procedures with regard to the number of deadline misses and energy consumption in presence of a domain shift. Furthermore, our HAM approach obtains results close to an explicit optimization for the target domain in a fraction of the necessary optimization time and without necessitating target labels.