The spectrum of scientific disciplines where computer-based simulation and prediction play a central role is broad: biology, medicine, high energy physics, climatology, astronomy, etc. In the near future, expected exascale supercom-puters will make it possible to address scientific problems that are more complex than ever. However, a major challenge on the path to such supercomputers is the required high energy-efficiency, i.e., maximizing the amount of computational work per watt. To answer this challenge, the position of this paper relies on compute nodes built from inherently low-power technologies. It considers 64-bit ARM processors combined with emerging non-volatile memory (NVM) technologies for main memory, known to be a bottleneck regarding performance and energy. DRAM technology is until now the mainstream option for main memory. However, it will hardly scale beyond a certain level because increased DRAM capacity requires higher refresh rates, which is harmful to power consumption. In such a context, emerging NVMs have become promising alternatives to DRAM thanks to their memory cell density and negligible leakage. This paper evaluates the impact of various main memory technologies, namely DDR4 SDRAM, Phase-Change Memory (PCM), Resistive RAM (RRAM), on computing system performance and memory-related energy consumption. The obtained results show that RRAM is a very promising candidate to mitigate main memory energy consumption, while PCM tends to represent a better candidate for storage level. Compared to DDR4 SDRAM, we observe RRAM can provide comparable system-level performance, while the main memory energy consumption can be reduced by up to 50%.