Optimization on IR-Drop Induced Accuracy Loss for Memristor-Based Neural Network

The analog properties of memristor cross array (MCA) can efficiently realize multiplication and accumulation (MAC) operation. Hence, MCA is widely used to construct the hardware accelerator of neuromorphic computing system. However, due to the nanowire resistance, the resistive network composed of the memristor and the nanowire suffers from IR-drop, which causes unavoidable loss in the output and hence affects the accuracy of neural network. In this paper, the relationships between the IR-drop of the memristor and its state, position, output current and output position are analyzed. Then, IR-drop is optimized by sparse mapping and output compensation is employed to further improve output accuracy. Experiments show that the optimization strategies proposed can effectively solve the IR-drop induced problem, and the recognition accuracy of the memristor-based neural network on MNIST dataset reaches 95.8%, with 33.5% improvement compared to the pre-optimization.