Effect of layer-specific synaptic retention characteristics on the accuracy of deep neural networks

Abstract

By modeling the change of weight/bias over time due to the retention behavior of charge trap device (CTD), we study the influence of synaptic retention characteristics on the inference accuracy of the deep neural network (DNN) considering the activation function and neural network type. After training the neural network composed of 3 fully-connected (FC) layers for the MNIST test and the VGG16 neural network for the CIFAR 10 test, the performance of neural networks is researched by changing weight/bias over time. The performance degradation of a neural network as weights/bias change over time depends on the type of activation function. Sensitivities to weight/bias loss differ depending on the depth of neural networks or the type of network layers.