Memristor-based Hopfield network circuit for recognition and sequencing application

Hopfield neural network has been widely used in image recognition because of its associative memory behavior. In this paper, a memristor neural network circuit is designed, which can recognize and sequence four characters simultaneously. It mainly includes three modules, namely a character recognition module, a signal processing module and a sequence module. The character recognition module consists of four individual character recognition units, corresponding to the recognition of four character images (W, H, A, T). The character recognition module includes calculation submodule and iteration submodule. After the operation of the calculation submodule and the iterative submodule, the four-character images distributed by noise can be identified simultaneously. The signal processing module is used to simplify the output signals of the character recognition module by four adder units. The sequence module ensures that stable state is eventually converged to the word (WHAT). The synapse weight circuit given in this paper can obtain different weights, so as to realize the function of associative memory. The iterative process circuit of Hopfield neural network is also designed to further demonstrate the iterative process. The neural network circuit composed of memristors maybe smaller, which may provide a reference for the development of new brain-like system.

Automated summary

A memristor neural network circuit is designed in this paper to recognize and sequence four characters simultaneously. Through the operation of calculation and iteration submodules, the four-character images distributed by noise can be identified simultaneously. This circuit may provide a reference for the development of new brain-like systems.