Extending correlation in branch prediction schemes

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/EURMIC.1999.794506The main aim of this research is to propose a new Two-Level Adaptive Branch Prediction scheme, based on additional correlation information. Conventional two-level adaptive branch prediction exploits the correlation between the outcome of a branch and the path followed through a program to reach the branch. Typically the program path is identified by recording whether each branch on the path is taken or not taken. Unfortunately, this limited information is insufficient to allow one path to a branch to be distinguished from other potential paths to the same branch. In this paper, we explore the benefits of adding sufficient information, in the form of successive branch addresses, to uniquely identify each program path. We use trace-driven simulation to compare our modified branch prediction scheme with a conventional GAp two-level predictor and demonstrate that our new predictor performs better than the conventional GAp scheme at the same level of hardware complexity.otherPeer reviewe