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In this paper we present an Application Customizable Branch Predictor, ACBP, that delivers efficiency in energy savings and performance without compromising prediction accuracy. The idea of our technique is to filter unnecessary global history information within the global history register to minimize the predictor size while maintaining prediction accuracy. We suggest in this work an efficient algorithm to capture the beneficial correlations. A cost-efficient and programmable hardware architecture is presented. Extensive experimental analysis confirms significant improvements in power savings and latency, ranging up to 84% and 30%,respectively.