Exploring variability and quantization effects in artificial neural networks using the MNIST dataset

This paper investigates the impact of introducing variability to trained neural networks and examines the effects of variability and quantization on network accuracy. The study utilizes the MNIST dataset to evaluate various Multi-Layer Perceptron configurations: a baseline model with a Single-Layer Perceptron and an extended model with multiple hidden nodes. The effects of Cycle-to-Cycle variability on network accuracy are explored by varying parameters such as the standard deviation to simulate dynamic changes in network weights. In particular, the performance differences between the Single-Layer Perceptron and the Multi-Layer Perceptron with hidden layers are analyzed, highlighting the network’s robustness to stochastic perturbations. These results provide insights into the effects of quantization and network architecture on accuracy under varying levels of variability. • Impact of Cycle-to-Cycle Variability of Resistive Random-Access Memory Devices in MNIST dataset classification. • Stochastic Modeling with Variable Neural Networks. • Effect of Quantization on Accuracy in Artificial Neural Networks.