Improving Self-Fault-Tolerance Capability of Memristor Crossbar Using a Weight-Sharing Approach

The ability of resistive memory (ReRAM) to naturally conduct vector-matrix multiplication (VMM), the primary operation carried out in neural networks, has caught the interest of researchers. The memristor crossbar is a suitable architecture to perform VMM and additionally offers benefits like in-memory computation (IMC), low power, and high density. Memristor-based neural networks are typically trained using a mechanism where weight computations are carried out on a host machine and downloaded into the crossbar. However, due to faulty memristors in the crossbar, a cell may not be able to store the exact weight values, which may lead to inference errors. In this paper, we propose a weight-sharing method to improve the self-fault-tolerance capability of memristor crossbar. In order to reduce the impact of faulty memristors, the weights are shared among different layers of memristors in a 3D crossbar. Simulation analyses show considerable improvements in the fault-tolerance capability of the crossbar.