OxRRAM-Based Analog in-Memory Computing for Deep Neural Network Inference: A Conductance Variability Study

Abstract

Analog in-memory compute (AiMC) is a promising approach to efficiently process deep neural networks (DNNs). Due to its small size and nonvolatility, oxide resistive random access memory (OxRRAM) is being considered as a memory device to map neural network weights in AiMC systems. However, OxRRAM conductance variability can have a severe impact on neural network accuracy. This variability depends on the conductance range, in which the OxRRAM device is programed. In this work, the conductance variability is characterized by a range of operating regimes. Next, the DNN accuracy is determined by applying the measured conductance distributions as noise on the neural network weights during training and inference. The software equivalent accuracy is achieved for only high current compliance values on small data sets. This has implications on the AiMC system, in which this device is intended to be used.