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As technology scales, more sophisticated fabrication processes cause variations in many different parameters in the device. These variations could severely affect the performance of processors by making the latency of circuits less predictable and thus requiring conservative design approaches. In this paper, we use Monte Carlo simulations in addition to worst-case circuit analysis to establish the overall delay due to process variations in a data cache sub-system under both typical and worst-case conditions. The distribution of the cache critical-path-delay in the typical scenario was determined by performing Monte Carlo simulations at different supply voltages, threshold voltages, and transistor lengths on a complete cache design. In addition to establishing the delay variation, we present an adaptive variable-cycle-latency cache architecture that mitigates the impact of process variations on access latency by closely following the typical latency behavior rather than assuming a conservative worst-case design-point. Simulation results show that our adaptive data cache can achieve a 9% to 31% performance improvement in a superscalar processor, on the SPEC2000 applications studied, compared to a conventional design. The area overhead for the additional circuits of the adaptive technique has less than 1% of the total cache area. Additional performance improvement potential exists in processors where the data cache access is on the critical path, by allowing a more aggressive clock rate.