Emergent Mind

Abstract

There has been a significant increase in leakage energy dissipation of CMOS circuits with each technology generation. Further, due to their large size, last level caches (LLCs) spend a large fraction of their energy in the form of leakage energy and hence, addressing this has become extremely important to meet the challenges of chip power budget. For addressing this, several techniques have been proposed. However, most of these techniques require offline profiling and hence cannot be used for real-life systems which usually run multitasking programs, with possible pre-emptions. In this paper, we propose a dynamic profiling based technique for saving cache leakage energy in multitasking systems. Our technique uses a small coloring-based profiling cache, to estimate performance and energy consumption of multiple cache configurations and then selects the best (least-energy) configuration among them. Our technique uses non-intrusive profiling and saves energy despite intra-task and inter-task variations; thus, it is suitable for multitasking systems. Simulations performed using workloads from SPEC2006 suite show the superiority of our technique over an existing cache energy saving technique. With a 2MB baseline cache, the average saving in memory sub-system energy is 22.8%.

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