Efficiently Switchable Context-Aware Dataflow Adaptation Technique for Low-Power Multi-Core Embedded Systems

Today's embedded systems operate under increasingly dynamic conditions. First, computational workloads can be either variable by nature or adjustable. Moreover, as many devices are battery-powered, it is common to have runtime power management technique, which results in dynamic power budget. This paper presents a design methodology for multi-core systems, based on dataflow specification, that can deal with various contexts. We optimize the original dataflow considering various working conditions, then, autonomously adapt it to a pre-defined optimal form in response to context changes. Such context changes at runtime might cause non-negligible delay or power budget violation. In order to overcome them, an efficient mode switching method is proposed. We show the effectiveness of the proposed technique with a real-life case study, stereo-vision, and synthetic benchmarks.