Processor Free Time Forecasting Based on Convolutional Neural Network

For a complex multitask and multiple low power mode processor, to make the processor enter a suitable low power mode in free time to reduce the power consumption, we can forecast the free time of the processor by Back Propagation (BP) neural network algorithm. But as a local search method and having the disadvantage being sensitive to initial weights, BP neural network has the low forecasting accuracy. In this paper, one dimensional convolution neural network is chosen to establish the forecasting model of free time, and some improvements are made based on two-dimensional convolution neural network to improve the suitableness for forecasting one-dimensional array. The simulation experiment shows that the accuracy of the data predicted by one dimensional convolutional neural network is higher than that of the BP neural network.