Global Adaptation for Energy Efficiency in Multicore Architectures

Today mixed-criticality systems are used in most industrial domains, because of their integration advantages. They are smaller, weigh less and reduce the idle time of previously dedicated hardware. However, these systems can still be improved. Since their hardware is now used more efficiently it automatically suffers more under the aging effects of heat created by all the simultaneous computations. Heat accelerates the aging process of hardware and increases failure rates. To prevent this the systems need to be cooled down by additional cooling devices like fans. In turn, these devices introduce new failure sources due to their movable parts. In this paper we propose a chip-wide approach to dynamically manage the system's computation and communication to optimize the energy-efficiency. By reducing the energy usage of the system we can reduce the additional hardware and therefore the weight of the whole system. Furthermore, we can prolong the system's lifetime as the available power resource lasts longer. We expand the current usage of tile-based energy management to a system wide scheme by implementing a meta-scheduler, which monitors the system state and changes the schedule if an optimization can be performed. This approach is shown to save up to 48% depending on the slack occurrence in an experimental setup.