Abstract
The rapid growth of IoT triggered the exponential growth of generated data transferred to/from Cloud and emerging Edge servers. As a result, recent projections forecast that the DRAM subsystem will soon be responsible for more than 40% of the overall power consumption within most servers [1]. One of the reasons for the high energy consumed by the DRAM devices is the usage of pessimistic DRAM operating parameters, such as voltage, refresh rate and timing parameters, set by the vendors. Vendors use these parameters to handle possible failures induced by the charge leakage and cell-to-cell interference. Moreover, such failures prevent scaling further scaling the size of DRAM cells. This reality has led researchers to question if such pessimistic parameters can be relaxed and if the induced failures can be handled at the hardware or software level. In this chapter, we discuss the challenges related to the DRAM reliability and present a systematic study on exceeding the conservative operating DRAM margins to improve the energy efficiency of Edge servers. We demonstrate a machine learning-based technique that enables us to scale down the DRAM operating parameters and the hardware/software stack that handles all the induced failures.
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Mukhanov, L., Karakonstantis, G. (2022). Improving DRAM Energy-efficiency. In: Karakonstantis, G., Gillan, C.J. (eds) Computing at the EDGE. Springer, Cham. https://doi.org/10.1007/978-3-030-74536-3_5
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