ReDHiP: Recalibrating Deep Hierarchy Prediction for Energy Efficiency

Abstract

Recent hardware trends point to increasingly deeper cache hierarchies. In such hierarchies, accesses that lookup and miss in every cache involve significant energy consumption and degraded performance. To mitigate these problems, in this paper we propose Recalibrating Deep Hierarchy Prediction (ReDHiP), an architectural mechanism that predicts last-level cache (LLC) misses in advance. An LLC miss means that all cache levels need not be accessed at all. Our design for ReDHiP focuses on a simple, compact prediction table that can be efficiently recalibrated over time. We find that a simpler scheme, while sacrificing accuracy, can be more accurate per bit than more complex schemes through recalibration. Our evaluation shows that ReDHiP achieves an average of 22% cache energy savings and 8% performance improvement for a wide range of benchmarks. ReDHiP achieves these benefits at a hardware cost of less than 1% of the LLC. We also demonstrate how ReDHiP can be used to reduce the energy overhead of hardware data prefetching while being able to further improve the performance.