A neuron circuit and a neural network circuit

Abstract

The invention discloses a neuron circuit including memristor elements, a trigger element, feedback elements and AND gates, wherein the memristor element is configured to receive an excitation signal,the trigger element is connected to the memristor element, and receives a clock control signal of the circuit and a signal outputted from the memristor element, the feedback element is used for connecting the output terminal of the trigger element and the input terminal of the memristor element, and controlling the voltage of the input terminal of the memristor element. The AND gate circuit is used for performing AND operation on the output signal of the trigger element and the clock control signal, and the output signal of the AND gate circuit is used as the output signal of the neuron circuit. The invention also relates to a neural network circuit.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a neuron circuit and a neural network circuit. Background technique [0002] Traditional neuron circuits are based on CMOS devices, and only consist of capacitors and dozens of transistors, which cannot be integrated on a large scale. Moreover, neuron circuits based on CMOS devices can only realize self-learning through external control circuits and software programming, that is, there is no inherent self-learning ability, and the power consumption is relatively high. [0003] In the prior art, although neuron circuits can be formed based on memristors, most neuron circuits use capacitors or passive components to realize the process of integral emission, which do not have cascading effects and driving capabilities, so in large In the large-scale integrated neural network, there is still a lack of neuron circuits that can match synaptic devices. Contents of the invention ...

AI Technical Summary

Problems solved by technology

[0002] Traditional neuron circuits are based on CMOS devices, and only consist of capacitors and dozens of transistors, which cannot be integrated on a large scale

Moreover, neuron circuits based on CMOS devices can only realize self-learning through external control circuits and software programming, that is, there is no inherent self-learning ability, and the power consumption is relatively high.

[0003] In the prior art, although neuron circuits can be formed based on memristors, most neuron circuits use capacitors or passive components to realize the process of integral emission, which do not have cascading effects and driving capabilities, so in large In large-scale integrated neural networks, there is still a lack of neuron circuits that can match synaptic devices

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