Analog Neural Network Inference Accuracy in One-Selector One-Resistor Memory Arrays

Abstract

Non-volatile memory arrays require select devices to ensure accurate programming. The one-selector one-resistor (1S1R) array where a two-terminal nonlinear select device is placed in series with a resistive memory element is attractive due to its high-density data storage; however, the effect of the nonlinear select device on the accuracy of analog in-memory computing has not been explored. This work evaluates the impact of select and memory device properties on the results of analog matrix-vector multiplications. We integrate nonlinear circuit simulations into CrossSim and perform end-to-end neural network inference simulations to study how the select device affects the accuracy of neural network inference. We propose an adjustment to the input voltage that can effectively compensate for the electrical load of the select device. Our results show that for deep residual networks trained on CIFAR-10, a compensation that is uniform across all devices in the system can mitigate these effects over a wide range of values for the select device I-V steepness and memory device On/Off ratio. A realistic I-V curve steepness of 60 mV/dec can yield an accuracy on CIFAR-10 that is within 0.44% of the floating-point accuracy.