Agile frequency scaling for adaptive power allocation in many-core systems powered by renewable energy sources

Abstract

Abstract-As low-power electronics and miniaturization conspire to populate the world with emerging devices, one appealing approach is to power these multi-core/many-core-based devices with energy harvested from various environments. Of the most important issues concerning these devices is how to effectively allocate power budget among the cores competing for power, which is formulated as one specific type of power-performance optimization problem in this paper. We attempt to solve this problem by proposing an Adaptive Power Allocation Technique (APAT) that explores a dynamic programming network. Our goal here is to maximize the overall system performance, taking into account a unique yet challenging fact that, available power budget might have to undergo a significant change when a renewable energy source is scavenging. APAT has a linear time complexity and low hardware overhead. Experiments have confirmed that APAT can reduce 20 ∼ 30% of execution time compared to other state-of-the-art power allocation algorithms. In addition, as APAT is quite insensitive to the changing rate of the power, lending itself well for power management in manycore systems powered by energy-harvesting sources.

An efficient runtime power allocation scheme for many-core systems inspired from auction theory

“…In the last step, the frequencies of the active tiles are set under the power budget, which can be achieved by the optimal dynamic programming network based method as reported in [29,30].…”

Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC

“…Another issue of an energy harvesting system is that the power constraint usually varies with time and the system must be adjusted accordingly in real-time. Therefore, it is necessary for a power management of WSN nodes to have a low cost overhead and can operate in real-time [ 14 ]. A task scheduling method considering power switching overhead is introduced in [ 15 ], a task splitting strategy is applied to improve the performance and a heuristic strategy is implemented to reduce the problem complexity.…”