Robust Ex-situ Training of Memristor Crossbar-based Neural Network with Limited Precision Weights.

Memristor crossbar-based neural networks perform parallel operation in the analog domain. Ex-situ training approach needs to program the predetermined resistance values in the memristor crossbar. Because of the stochasticity of the memristor devices, programming a memristor needs to read the device resistance value iteratively. Reading a single memristor in a crossbar (without isolation transistor) is challenging due to the sneak path current. Programming a memristor in a crossbar to either RON or ROFF state is relatively straightforward. A neural network implemented using higher precision weights provides higher classification accuracy compared to a Ternary Neural Network (TNN). This paper demonstrates the implementation of memristor-based neural networks using only the two resistance values (RON, ROFF). At the same time, it achieves higher weight precision. The experimental result shows that the proposed higher precision synapses are easy to program and provide better classification accuracy compared to a TNN.