Design Aid of Multi-core Embedded Systems with Energy Model

Abstract

Shifting to multi-core designs is so pervasive a trend to overcome the power wall and it is a necessary move for embedded systems in our rapidly evolving information society. Meanwhile, the need to increase the battery life and reduce maintenance costs for such embedded systems is very critical. Therefore, a wide variety of power reduction techniques have been proposed and realized, including Clock Gating, DVFS and Power Gating. To maximize the effectiveness of these techniques, task scheduling is a key but for multi-core systems it is very complicated due to the huge exploration space. This problem is a major obstacle for further power reduction. To cope with it, we propose a design method for embedded systems to minimize their energy consumption under performance constraints. This method is based on the clarification of properties of the above mentioned low power techniques and their interactions. In more details, we firstly establish energy models for these low power techniques and our target systems. We then explore for the best configuration by constructing an optimization problem especially for applications which have a longer deadline than the execution interval. Finally, we propose an approximate solution using dynamic programming with a lower computation complexity and compare it to a brute force explicit solution. We confirm with our evaluations that the proposed method successfully found a better configuration which reduces the total energy consumption by 32% if compared to the manually optimized configuration, which utilizes only one core.

An Energy-Efficient Task Scheduling for Near Real-Time Systems on Heterogeneous Multicore Processors

“…We adopt a lumped execution [17], which executes multiple jobs continuously. To realize this execution, a job is not executed immediately after it arrives but postponed until several jobs arrive.…”

An Energy-Efficient Task Scheduling for Near-Realtime Systems with Execution Time Variation

“…We solve this optimization problem in the following sections. In this paper, we adopt a lumped execution [20], which executes multiple tasks continuously. To realize this execution, we do not execute a task just after it is available.…”