Tackling the dark silicon problem in a heterogeneous multicore system, the temperature constraints across the system should be addressed carefully by assigning a proper set of tasks to a pool of the heterogeneous cores during the run-time. When such a system is utilized in a reliable/real-time application, the reliability/timing constraints of the application should also be augmented to the temperature constraints and make the tasks mapping problem more and more complex. To solve the mapping problem in such a situation, we propose READY; an online reliability- and deadline-aware mapping and scheduling algorithm for heterogeneous multicore systems. READY utilizes an adaptive power constraint (as a metric for temperature measurement) that is updated according to the number and position of the active cores on the chip. READY, first, attempts to meet the reliability target of the system by improving the reliability of each task. Then, it performs the mapping and scheduling of the tasks on cores of different islands, so that the peak power and timing constraints are met. The simulation results illustrate that while READY guarantees the timing constraints and meets reliability targets, it improves the peak-power-aware system schedulability (chip performance) by 23.77% (up to 40.69%).

Analysis of reliability, power, and performance at hardware and software levels due to heterogeneity is a crucial requirement for heterogeneous multicore embedded systems. Escalating power densities have led to thermal issues for heterogeneous multicore embedded systems. This paper proposes a peak-power-aware reliability management scheme to meet power constraints through distributing power density on the whole chip such that reliability targets are satisfied. In this paper, we consider peak power consumption as a system-level power constraint to prevent system failure. To balance the power consumption, we also employ a Dynamic Frequency Scaling (DFS) method to further reduce peak power consumption and satisfy thermal constraints on the chip. We illustrate the benefits of our scheme by comparing it with state-of-the-art schemes, resulting in average in 26.5 percent less peak power consumption (up to 54.3 percent).