Toward application-specific memory reconfiguration for energy efficiency

The end of Dennard scaling has made energy-efficiency a critical challenge in the continued increase of computing performance. An important approach to increasing energy-efficiency is hardware customization. In this study we explore the opportunity for energy-efficiency via memory hierarchy customization and present a methodology to identify application-specific energy efficient configurations. Using a workload of 37 diverse benchmarks, we address three key questions: 1) How much energy saving is possible?, 2) How much reconfiguration is required?, and 3) Can we use application characterization to automatically select an energy-optimal memory hierarchy configuration? Our results show that the potential benefit is large -- average reductions close to 70% in memory hierarchy energy with no performance loss. Further, our results show that the number of configurations need not be large; 13 carefully chosen configurations can deliver 93% of this benefit (64% energy reduction), and even coarse-grain reconfigurations of an existing hierarchy can deliver 81% of this benefit (56% energy reduction), suggesting that reconfigurable hierarchies may be practically realizable. Finally, as a first step towards automatic reconfiguration, we explore application characterization via reuse distance as a guide to select the best memory hierarchy configuration; we show that reuse distance can effectively predict the application-specific configuration which will both maintain performance and deliver energy efficiency.