A neuromorphic ASIC design using one-selector-one-memristor crossbar
The applications of memristors in neuromorphic computing have been extensively studied for its analogy to synapse. To overcome sneak path issue, nonlinear resistive selectors have been introduced to the design of memristor crossbar, enabling a high integration density and robust computing capability. However, the nonlinearity of such selectors also influences the computation accuracy of the vector-matrix multiplication performed on the memristor crossbar. In this work, we evaluate the impact of nonlinear resistive selectors on the computation robustness of a Hopfield spike-based pattern recognition system based on memristor crossbar technology. The methods that can suppress the adverse impact of the nonlinear selector on the system performance are also studied.
