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Front-end Instruction delivery accounts for a significant fraction of the energy consumed in a dynamic superscalar processor. The Issue queue in these processors serves two crucial roles: it bridges the front and back ends of the processor and serves as the window of instructions for the out-of-order engine. A mismatch between the front end producer rate and back end consumer rate, and between the supplied instruction window from the front end, and the required instruction window to exploit the level of application parallelism, results in additional front-end energy, and Increases the issue queue utilization. While the former increases overall processor energy consumption, the latter aggravates the issue queue hot spot problem. We propose a complementary combination of fetch gating and issue queue adaptation to address both of these issues. We introduce an issue-centric fetch gating scheme based on issue queue utilization and application parallelism characteristics. Our scheme attempts to provide an instruction window size that matches the current parallelism characteristics of the application while maintaining enough queue entries to avoid back-end starvation. Compared to a conventional fetch gating scheme based on flow-rate matching, we demonstrate 20% better overall energy-delay with a 44% additional reduction in issue queue energy. We identify Icache energy savings as the largest contributor to the overall savings and quantify the sources of savings in this structure. We then couple this issue-driven fetch gating approach with an issue queue adaptation scheme based on queue utilization. While the fetch gating scheme provides a window of issue queue instructions appropriate to the level of program parallelism, the Issue queue adaptation approach shuts down the remaining underutilized issue queue entries. Used in tandem, these complementary techniques yield a 20% greater issue queue energy savings than the addition of the savings from each technique applied in isolation. The result of this combined approach is a 6% overall energy-delay savings coupled with a 54% reduction in issue queue energy.