Effect of conductance linearity of Ag-chalcogenide CBRAM synaptic devices on the pattern recognition accuracy of an analog neural training accelerator

Abstract

Abstract Pattern recognition using deep neural networks (DNN) has been implemented using resistive RAM (RRAM) devices. To achieve high classification accuracy in pattern recognition with DNN systems, a linear, symmetric weight update as well as multi-level conductance (MLC) behavior of the analog synapse is required. Ag-chalcogenide based conductive bridge RAM (CBRAM) devices have demonstrated multiple resistive states making them potential candidates for use as analog synapses in neuromorphic hardware. In this work, we analyze the conductance linearity response of these devices to different pulsing schemes. We have demonstrated an improved linear response of the devices from a non-linearity factor of 6.65 to 1 for potentiation and −2.25 to −0.95 for depression with non-identical pulse application. The effect of improved linearity was quantified by simulating the devices in an artificial neural network. The classification accuracy of two-layer neural network was seen to be improved from 85% to 92% for small digit MNIST dataset.