ESKIMO - energy savings using semantic knowledge of inconsequential memory occupancy for DRAM subsystem

Dynamic random access memory (DRAM) is used as the bulk of the main memory in most computing systems and its energy and power consumption has become a first-class design consideration for modern systems. We propose ESKIMO, a scheme where when the program or operating systems memory manager allocates or frees up a memory region, this information is used by the architecture to optimize the working of the DRAM system, particularly to save energy and power. In this work we attempt to have the architecture work hand in hand with information about allocated and freed space provided by the program. We discuss multiple ways to use this information to reduce the energy and power consumption of the memory and present results of this optimization. We evaluate the energy and power benefits of our technique using a publicly available, hardware-validated, DRAM simulator, DRAMsim. Our current studies show very promising results with energy savings on average of 39%.