A Hybrid Neural Based Dynamic Branch Prediction Unit | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 30, Issue 6, March 2012, Pages 1066-1081 PDF (238.95 K) | ||
| DOI: 10.30684/etj.30.6 12 | ||
| Author | ||
| Gheni A. Ali | ||
| Abstract | ||
| Modern high performance processor architectures have come to depend upon highly pipelined operation in order to achieve improvements in operating speed. As a result, the cost associated with flushing the pipeline and refilling it when a branch instruction is mis-predicted can significantly impact processor performance. Many schemes, from the extremely simple to the highly complex, have been proposed to improve branch prediction accuracy. Conventional two-level branch predictors predict the outcome of a branch either based on the( local branch history) information, comprising the previous outcomes of a single branch (intra-branch correlation), or based on the (global branch history) information, comprising the previous outcomes of all branches (inter-branch correlation). The misprediction rates for these predictors are very high when they predict branch instructions with hybrid correlations. In this paper we suggest a hybrid perceptron based predictor which employs up to 31-bits of both local and global branch history information to minimize the misprediction rates. The software written for simulation and testing shows that the suggested hybrid predictor achieves a high accuracy. Our results shows that the best response of the predictor is obtained on history length of 16- bits. | ||
| Keywords | ||
| Branch Prediction; Advanced Processor Architecture; neural networks; Pipelining | ||
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