Power Optimization of Embedded Memory Systems via Data Remapping

In this paper, we provide a novel compile-time data remapping algorithm that runs in linear time. This remapping algorithm is the first fully automatic approach applicable to pointer-intensive dynamic applications. We show that data remapping can be used to significantly reduce the energy consumed as well as the memory size needed to meet a user-specified performance goal (i.e., execution time) -- relative to the same application executing without being remapped. These twin advantages afforded by a remapped program -- improved cache and energy needs -- constitute a key step in a framework for design space exploration: for any given performance goal, remapping allows the user to reduce the primary and secondary cache size by 50%, yielding a concomitant energy savings of 57%. Additionally, viewed as a compiler optimization for a fixed processor, we show that remapping improves the energy consumed by the cache subsystem by 25%. All of the above savings are in the context of the cache subsystem in isolation. We also show that remapping yields an average 20% energy saving for an ARM-like processor and cache subsystem. All of our improvements are achieved in the context of DIS, OLDEN and SPEC2000 pointer-centric benchmarks