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Tag comparison in a highly associative cache consumes a significant portion of the cache energy. Existing methods for tag comparison reduction are based on predicting either cache hits or cache misses. In this paper, we present novel ideas for both cache hit and miss predictions. We present a partial tag-enhanced Bloom filter to improve the accuracy of the cache miss prediction method and hot/cold checks that control data liveness to reduce the tag comparisons of the cache hit prediction method. We also combine both methods so that their order of application can be dynamically adjusted to adapt to changing cache access behavior, which further reduces tag comparisons. To overcome the common limitation of multistep tag comparison methods, we propose a method that reduces tag comparisons while meeting the given performance bound. Experimental results showed that the proposed method reduces the energy consumption of tag comparison by an average of 88.40%, which translates to an average reduction of 35.34% (40.19% with low-power data access) in the total energy consumption of the L2 cache and a further reduction of 8.86% (10.07% with low-power data access) when compared with existing methods.