Dynamic reconfigurable logic gate realization method based on cellular neural network (CNN) and realization circuit thereof

Abstract

The invention discloses a dynamic reconfigurable logic gate realization method based on a cellular neural network (CNN) and a realization circuit thereof. An input signal, template parameters and offset parameters are utilized to construct a state equation, an equation of system state variables is achieved, and different logical functions can be realized dynamically only through changing of template parameter signals. The dynamic reconfigurable logic gate realization method based on the CNN and the realization circuit of the dynamic reconfigurable logic gate realization method based on the CNN have the advantages of being simple in circuit, high in system stability and suitable for realization of super-large-scale integrated circuits.

Description

technical field [0001] The invention relates to the technical field of reconfigurable circuits, in particular to a method for realizing a dynamic reconfigurable logic gate based on a cellular neural network (CNN) and a realization circuit thereof. Background technique [0002] The logic gate circuit is the most basic logic element in the digital circuit. The so-called gate is a kind of switch, which can control the passing or not passing of the signal according to certain conditions. There is a certain logical relationship (causal relationship) between the input and output of the gate circuit, so the gate circuit is also called a logic gate circuit. Traditional logic gate circuits are composed of single-selection switch circuits. The gate circuits are pre-set and cannot be dynamically converted, such as: "AND gate" and "OR gate" are not interchangeable. The computer working system formed by this structure is facing a development dilemma. Users cannot control the configurati...

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Problems solved by technology

However, the chaotic system is quite complex and is very sensitive to system parameters and initial values, which makes the system not stable and not easy to implement in ultra-large-scale circuits.

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