Characterizing the Opportunity and Feasibility of Reconfigurable Memory Hierarchies for Improved Energy Efficiency

Pietro Cicotti; Laura Carrington; Andrew A. Chien. 21 August, 2013.
Communicated by Andrew Chien.


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 to specific application needs. In this study we explore the opportunity for memory hierarchy customization for energy-efficiency, exploring reconfigurable memory hierarchies. Using a workload of 37 diverse benchmarks, we address three key questions: 1) how much benefit is possible?, 2) how much reconfiguration is required?, and 3) can we automatically select a good memory hierarchy configuration? Our results show that the potential benefit is large average reductions of 70% in memory hierarchy energy with no performance loss. Further, our results show that number of configurations need not be large; ten carefully chosen configurations can deliver 90% of this benefit (63% energy reduction) suggesting that configurable hierarchies may be practically realizable. Finally, we explore reuse distance as a guide to select the best memory hierarchy configuration as a first step towards automatic configuration, and show that it can effectively predict which memory hierarchies will both maintain performance and deliver energy efficiency.

Original Document

The original document is available in PDF (uploaded 21 August, 2013 by Andrew Chien).