Training Set Optimization in an Artificial Neural Network Constructed for High Bandwidth Interconnects Design

Abstract

In this article, a novel training set optimization method in an artificial neural network (ANN) constructed for high bandwidth interconnects design is proposed based on rigorous probability analysis. In general, the accuracy of an ANN is enhanced by increasing training set size. However, generating large training sets is inevitably time-consuming and resource-demanding, and sometimes even impossible due to limited prototypes or measurement scenarios. Especially, when the number of channels in required design are huge such as graphics double data rate (GDDR) memory and high bandwidth memory (HBM). Therefore, optimizing the training set selection process is crucial to minimizing the training datasets for developing an efficient ANN. According to rigorous mathematical analysis of the uniformity of the training data by probability distribution function, optimization flow of the range selection is proposed to improve accuracy and efficiency. The optimal number of training data samples is further determined by studying the prediction error rates. The performance of the proposed method in terms of accuracy is validated by comparing the scattering parameters of arbitrarily chosen strip and microstrip type GDDR interconnects obtained from EM simulations with those predicted by ANNs using default and the proposed training-set selection methods.