Scenario-Based Soft Real-Time Hybrid Application Mapping for MPSoCs

For soft real-time applications, a fixed mapping to a heterogeneous MPSoC architecture can lead to high energy consumption and even deadline misses if tasks have input-dependent execution times. Here, specialized mappings are required that, e.g., map tasks with high execution times for the current input to resources with high computational power as they else may cause deadline misses. However, optimizing mappings for both energy and latency at run time is too compute-intensive. As a remedy, we propose a hybrid application mapping technique suited for blackbox applications, i.e., no information about functional behaviors is available. It is based on clustering input data evoking similar workloads into so-called workload scenarios. At design time, we optimize the scenario distribution and their associated mappings regarding energy consumption and latency by an iterative design space exploration. At run time, a machine-learning-based runtime manager first identifies the scenario of the current input by monitoring its non-functional execution properties. Based on these identified scenarios, a mapping for subsequent data processing is selected so that missed deadlines and the energy are minimized. Evaluations performed based on two dynamic applications show that the proposed hybrid application mapping procedure consistently outperforms state-of-the-art mapping approaches with regard to both deadline misses and energy consumption.