Branch Prediction Techniques and Optimizations

Branch prediction is one of the ancient performance improving techniques which still finds relevance into modern architectures. While the simple prediction techniques provide fast lookup and power efficiency they suffer from high misprediction rate. On the other hand, complex branch predictions – either neural based or variants of two-level branch prediction – provide better prediction accuracy but consume more power and complexity increases exponentially. In addition to this, in complex prediction techniques the time taken to predict the branches is itself very high – ranging from 2 to 5 cycles – which is comparable to the execution time of actual branches. Branch prediction is essentially an optimization (minimization) problem where the emphasis is on to achieve lowest possible miss rate, low power consumption and low complexity with minimum resources. In this survey paper we review the traditional Two-level branch prediction techniques; their variants and the underlying principles which make them predict accurately. We also briefly discuss the Perceptron based technique which uses lightweight neural network technique to predict the outcome of branches.