Branch Prediction Reversal by Correlating with Data Values

Branch prediction is one of the main hurdles in the roadmap towards higher clock frequencies and deeper pipelines. This work presents a new approach to enhancing current branch predictors: Selective Branch Prediction Reversal. The rationale behind this proposal is the fact that many branch mispredictions can be avoided if branch prediction is selectively reversed. We present a Branch Prediction Reversal Unit (BPRU) that selectively reverses branch predictions by correlating with the predicted values of the branch inputs, in addition to recent control flow. As a case study, we have included the BPRU in an already proposed branch predictor, the Branch Predictor through Value Prediction (BPVP). The effect is a reduction by half in its original misprediction rate. We have also measured the improvement when the BPRU is used in a hybrid scheme composed of a BPVP and a gshare predictors. Results using immediate updates show average reductions in misprediction rate ranging from 7% to 14%. Performance evaluation of the proposed BPRU in a 20- stage superscalar processor shows an IPC improvement of up to 9%.