A Dynamic Branch Predictor Based on Parallel Structure of SRNN

Branch predictor is a key component of processor, which can improve the efficiency of instruction execution. The branch predictor based on machine learning algorithm can achieve high branch prediction accuracy, but it has the disadvantages of long training time and high access delay. As a neural network algorithm, Recurrent Neural Network (RNN) is good at processing data related to time series, and can learn the correlation between data faster. Sliced Recurrent Neural Network (SRNN) parallelizes the RNN algorithm, effectively reducing the access delay of the RNN algorithm. In this paper, a dynamic branch predictor based on parallel structure of SRNN is proposed to accelerate the training time and reduces the computing delay. The optimal design parameters of predictor, which has prediction accuracy with lower source cost, are selected through a serial simulations. The experimental results show that the branch predictor proposed in this paper has higher prediction accuracy than the traditional Bimod and Gshare branch predictors under the same hardware consumption, and its branch prediction rate is 2.34% higher than the traditional Perceptron neural predictor in the short learning period.