In-Memory Computing Circuit Implementation of Complex-Valued Hopfield Neural Network for Efficient Portrait Restoration
Qinghui Hong, Haotian Fu, Yiyang Liu, Jiliang Zhang
Abstract
Complex-valued neural networks have better optimization capabilities, stronger robustness, and richer characterization capabilities compared with real-valued neural networks, which has achieved good results in the field of portrait restoration. However, there is almost no circuit implementation of complex-valued neural networks. Based on this, this article proposes an in-memory computing circuit implementation of a complex-valued Hopfield neural network (CHNN) for the first time, which provides a highly accurate and efficient processing circuit for portrait restoration. First, a new memristive array is proposed, which can realize parallel complex-valued multiplication and complex-valued vector–matrix multiplication. On the basis, a CHNN circuit that can perform large-scale recursive computations is designed. Due to the characteristics of in-memory computation, the computation speed and robustness have been improved when realizing portrait restoration. Different portrait restoration scenarios can be realized based on the programmability of the memristive array. Pspice simulation results show that the recovery speed of CHNN can reach the level of 0.1 ms, and the accuracy can reach above 97.00%. Robustness analysis shows that the circuit can tolerate a certain degree of programming error and has strong anti-noise performance.