Architectural Leakage-Aware Management of Partitioned Scratchpad Memories

Partitioning a memory into multiple blocks that can be independently accessed is a widely used technique to reduce its dynamic power. For embedded systems, its benefits can be even pushed further by properly matching the partition to the memory access patterns. When leakage energy comes into play, however, idle memory blocks must be put into a proper low-leakage sleep state to actually save energy when not accessed. In this case, the matching becomes an instance of power management problem, because moving to and from this sleep state requires additional energy. In this work, we propose an explorative solution to the problem of leakage-aware partitioning of a memory into disjoint sub-blocks. In particular, we target scratchpad memories, which are commonly used in some embedded systems as a replacement of caches. We show that the total energy (dynamic and static) cost function yields a non-convex partitioning space, making smart exploration the only viable option; we propose an effective randomized search in the solution space which has very good match with the results of exhaustive exploration, when this is feasible. Experiments on a different sets of embedded applications has shown that total energy savings larger than 60% on average can be obtained, with a marginal overhead in execution time, thanks to an effective implementation of the low-leakage sleep state