Towards a high performance neural branch predictor

The main aim of this short paper is to propose a new branch prediction approach called by us "neural branch prediction". We developed a first neural predictor model based on a simple neural learning algorithm, known as Learning Vector Quantization algorithm. Based on a trace driven simulation method we investigated the influences of the learning step and training processes. Also we compared the neural predictor with a powerful classical predictor and we establish that they result in close performances. Therefore, we conclude that in the nearest future it might be necessary to model and simulate other more powerful neural adaptive predictors, based on more complex neural networks architectures or even time series concepts, in order to obtain better prediction accuracies compared with the previous known schemes.