Evaluating Device Variability in RRAM-Based Single- and Multi-Layer Perceptrons

A. Blumenstein (THM Univ. of Applied Sciences, Germany and Univ. Rovira i Virgili, Spain), E. Pérez, C. Wenger (IHP Frankfurt (Oder) and Brandenburg Univ. of Techn. Cottbus - Senftenberg, Germany), N. Dersch (THM Univ. of Applied Sciences, Germany and Univ. Rovira i Virgili, Spain), A. Kloes (THM Univ. of Applied Sciences, Germany), B. Iñíguez (Univ. Rovira i Virgili, Spain), M. Schwarz (THM Univ. of Applied Sciences, Germany)

This work investigates the impact of stochastic weight variations in hardware implementations of artificial neural networks, focusing on a Single-Layer Perceptron and Multi-Layer Perceptrons. A variable neural network model is introduced, applying Gaussian variability to synaptic weights based on an adjustment rate, which controls the proportion of affected weights. By studying how stochastic variations affect accuracy, simulations under device-to-device and cycle-to-cycle variation conditions demonstrate that Single-Layer Perceptrons are more sensitive to weight variations, while Multi-Layer perceptrons show greater robustness. Additionally, stochastic quantization improves the performance of Multi-Layer Perceptrons but has minimal effect on Single-Layer Perceptrons.