Energy-Aware Real-Time Task Scheduling on Local/Shared Memory Systems

The rapid development of the Internet of Things (IoT) has increased the requirement on the processing capabilities of sensors, mobile phones and smart devices. Meanwhile, energy efficiency techniques are in desperate need as most devices in the IoT systems are battery powered. Following the above two trends, this work explores the memory system energy efficiency for a general multi-core architecture. This architecture integrates a local memory in each processing core, with a large off-chip memory shared among multiple cores. Decisions need to be made on whether tasks will be executed with the shared memory or the local memory to minimize the total energy consumption within real-time constraints. This paper proposes optimal schemes as well as a polynomial-time approximation algorithm with constant ratio. The complexity analysis of the problem for different task and system models is also presented. Experimental results show that the proposed approximation algorithm performs close to the optimal solution in average.