Memristive neural network circuit design based on locally competitive algorithm for sparse coding application

Abstract

Sparse coding can quickly, accurately, and inexpensively represent the stimulus information received by biological vision neurons. However, there is no entire circuit that can realize real-time sparse coding by efficient analog computation. A memristor neural network circuit that solves the sparse coding problem in real-time and in parallel is proposed to solve such a problem. In our circuit, a novel memristor array structure can realize both reading and writing in parallel. A neural network circuit that can execute the Locally Competitive Algorithm (LCA) is designed based on this structure. Given these designs, the proposed neural networks circuit can utilize the programmability of the memristor array to real-time process various sparse coding problems. Based on the proposed circuit, the module can process binary and grayscale image sparse coding, providing a circuit implementation platform for sparse coding tasks. The simulation results demonstrate that the processing speed of sparse coding is improved compared with the MATLAB simulation and the application robustness is enhanced.