Improving memory energy using access pattern classification

In this paper, we propose a data-driven strategy to optimize the memory energy consumption in a banked memory system. Our compiler-based strategy modifies the original execution order of loop iterations in array-dominated applications to increase the length of the time period(s) in which memory banks axe idle (i.e., not accessed by any loop iteration). To achieve this it first classifies loop iterations according to their bank access patterns and then, with the help of a polyhedral tool, tries to bring the iterations with similar bank access patterns close together. Increasing the idle periods of memory banks brings two major benefits; first, it allows us to place more memory banks into low-power operating modes, and second, it enables us to use a more aggressive (i.e., more energy saving) operating mode for a given bank. Our strategy has been evaluated using seven array-dominated applications on both a cacheless system and a system with cache memory. Our results indicate that the strategy is very successful in reducing the memory system energy, and improves the memory energy by as much as 34% on the average.