Cache decay: exploiting generational behavior to reduce cache leakage power

Abstract

<i>Power dissipation is increasingly important in CPUs ranging from those intended for mobile use, all the way up to high-performance processors for high-end servers. While the bulk of the power dissipated is dynamic switching power, leakage power is also beginning to be a concern. Chipmakers expect that in future chip generations, leakage's proportion of total chip power will increase significantly.</i> <i>This paper examines methods for reducing leakage power within the cache memories of the CPU. Because caches comprise much of a CPU chip's area and transistor counts, they are reasonable targets for attacking leakage. We discuss policies and implementations for reducing cache leakage by invalidating and &#8220;turning off&#8221; cache lines when they hold data not likely to be reused. In particular, our approach is targeted at the generational nature of cache line usage. That is, cache lines typically have a flurry of frequent use when first brought into the cache, and then have a period of &#8220;dead time&#8221; before they are evicted. By devising effective, low-power ways of deducing dead time, our results show that in many cases we can reduce LI cache leakage energy by 4x in SPEC2000 applications without impacting performance. Because our</i> decay-based <i>techniques have notions of competitive on-line algorithms at their roots, their energy usage can be theoretically bounded at within a factor of two of the optimal oracle-based policy. We also examine <i>adaptive</i> decay-based policies that make energy-minimizing policy choices on a per-application basis by choosing appropriate decay intervals individually for each cache line. Our proposed adaptive policies effectively reduce LI cache leakage energy by 5x for the SPEC2000 with only negligible degradations in performance</i>.

DOI: 10.1145/379240.379268

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@inproceedings{Kaxiras2001CacheDE, title={Cache decay: exploiting generational behavior to reduce cache leakage power}, author={Stefanos Kaxiras and Zhigang Hu and Margaret Martonosi}, booktitle={ISCA}, year={2001} }