Recent Internet-of-Things or Wireless Sensor Network devices are often operated with energy harvesters. As there are no energy storages in those devices, power is not consistently provided to the devices at all times. In such transiently powered systems, in order to keep the system reliable without losing any execution contexts, non-volatile memories (NVMs) are typically used for swift backup/restoration of execution contexts. In this article, we perform a real-time schedulability analysis of the transiently powered processors with NVMs. We first quantitatively characterize the charging and discharging behaviors of the energy harvester and extract the compute capability of the system in time interval domain. Then, based on Real-Time Calculus, we determine whether the given multi-task workload is schedulable or not with respect to the earliest deadline first (EDF) or fixed-priority (FP) scheduling policies. In addition, we study how the choice of the threshold voltage parameter affects the schedulability, then propose a feasible threshold selection algorithm to enhance schedulability. We verify the effectiveness of the proposed technique with extensive simulations. Compared to the naive selection method, the proposed technique always shows improvements in schedulability in various workloads.

Today's embedded systems operate under increasingly dynamic conditions. First, computational workloads can be either fluctuating 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. We show the effectiveness of the proposed technique with a real-life case study and synthetic benchmarks.