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.

Memristor-based crossbar architectures have been explored by researchers for neuromorphic computing, where analog vector-matrix multiplication can be carried out in a single time step. In this paper we explore such architectures for carrying out various arithmetic operations. Since the computations are carried out in analog domain, they are affected by fabrication and performance variability of the manufactured devices. As a result, there can be inherent errors during the computation. However, the architecture can be suitable for approximate computing applications where some errors can be tolerated. We have proposed a method for carrying out arithmetic operations with any multiple of k-bit resolution on the crossbar, for some limited values of k. The fault tolerant capability of the proposed architecture is evaluated through experimentation on benchmark datasets. We also perform case studies to analyze the performance of the approach with particular emphasis on approximate computing. The results of the case studies show that certain applications indeed exhibit fault tolerance in presence of faulty memristors.