Circuit and microarchitectural techniques for reducing cache leakage power

  • Authors:
  • Nam Sung Kim;Krisztian Flautner;David Blaauw;Trevor Mudge

  • Affiliations:
  • Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI;ARM, Ltd., Cambridge CB1 9NJ, U.K.;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI

  • Venue:
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Year:
  • 2004

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Abstract

On-chip caches represent a sizable fraction of the total power consumption of microprocessors. As feature sizes shrink, the dominant component of this power consumption will be leakage. However, during a fixed period of time, the activity in a data cache is only centered on a small subset of the lines. This behavior can be exploited to cut the leakage power of large data caches by putting the cold cache lines into a state preserving, low-power drowsey mode. In this paper, we investigate policies and circuit techniques for implementing drowsy data caches. We show that with simple microarchitectural techniques, about 80%-90% of the data cache lines can be maintained in a drowsy state without affecting performance by more than 0.6%, even though moving lines into and out of a drowsy state incurs a slight performance loss. According to our projections, in a 70-nm complementary metal-oxide-semiconductor process, drowsy data caches will be able to reduce the total leakage energy consumed in the caches by 60%-75%. In addition, we extend the drowsy cache concept to reduce leakage power of instruction caches without significant impact on execution time. Our results show that data and instruction caches require different control strategies for efficient execution. In order to enable drowsy instruction caches, we propose a technique called cache subbank prediction, which is used to selectively wake up only the necessary parts of the instruction cache, while allowing most of the cache to stay in a low-leakage drowsy mode. This prediction technique reduces the negative performance impact by 78% compared with the no-prediction policy. Our technique works well even with small predictor sizes and enables a 75% reduction of leakage energy in a 32-kB instruction cache.