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With the growth of the Internet of Things (IoT), increasingly, more computing tasks are implemented on power-sensitive mobile devices, causing a bottleneck on energy consumption. Most mobile devices consume considerable power in the standby mode, during which the capacitive DRAM cells' self-refresh power, which is used to preserve data integrity, accounts for a large part. To address this issue, strategies from both hardware and software perspectives have been proposed, and yet, the existing hardware methods usually have a high cost. Software strategies mainly focus on the operating system page allocation strategy to maximize resource idleness. The partial array self-refresh technique has been proposed to allow for some of the DRAM cells to retain the self-refresh state while shutting down the others. Using this technique, numerous studies extend the unused DRAM idle time by clustering the applications' data but do not discuss the impact of the address mapping mechanism on power consumption. However, this impact was proven to be essential in our previous study. In this paper, we attempt to investigate the impacts of different memory mapping schemes on different clustering strategies using a hierarchy-based memory management system (HBMM) and provide insights for selecting the optimal schemes. The experimental results on the Android platform demonstrate that the schemes and strategies are closely correlated, and memory idle time can be prolonged 125 times with their suitable combination. Conversely, the worst scheme may decrease memory idleness by 12% compared with the original system. Furthermore, our studies on a physical platform show that standby power consumption can be saved by 23% when using HBMM combined with the best clustering strategy. |
