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Memristive recurrent neural network circuit

A recursive neural network and circuit technology, applied in the field of memristive recurrent neural network circuit, can solve the problems of high power consumption of the circuit, unfavorable circuit integration, etc., and achieve the effects of wide application, increased integration and high flexibility

Active Publication Date: 2021-10-01
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

[0006] The synapses in the above two Hopfield neural networks use resistors. Once the resistance value is fixed, the function of the entire network will be fixed accordingly. Since the weights of the synapses cannot be programmed flexibly, this kind of circuit will suffer greatly in application. At the same time, a large number of nonlinear devices are used in the discrete Hopfield neural network and the continuous Hopfield neural network, the power consumption of the entire circuit will be high and it is not conducive to the integration of the circuit

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Embodiment 1

[0059] Embodiment 1, the design of neuron module circuit. like Figure 5 As shown, it is a schematic diagram of the circuit structure of the neuron module in this embodiment. This part is mainly composed of MOSFSET transistors (hereinafter referred to as field effect transistors) and capacitors. Field effect transistor Q1 is used as an input, and field effect transistor Q2 and field effect transistor Q3 are used as input. Connected in series as a feedback signal, the gates of field effect transistors Q4 and Q5 are connected, similarly, the gates of field effect transistors Q6 and Q7 are connected, and the source of Q5 is connected to the gate of Q6. The basic principle is that when a presynaptic neuron transmits a signal to Vin through a synapse, capacitors C1 and C2 act as membrane capacitors to integrate the current until the anode voltage Vc1 of C1 exceeds the threshold voltage Vth1 of inverters Q4 and Q5. Then, the inverters Q6 and Q7 output a high voltage, at this time t...

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Abstract

The invention provides a memristive recurrent neural network circuit, which comprises a neuron module circuit, a reverse summation circuit and a memristive cross synaptic array circuit, wherein a pre-synaptic neuron in the memristive cross synaptic array circuit transmits a current signal to the input end of the neuron module circuit through a memristor; and the output end of the neuron module circuit transmits a current signal to the reverse summing module circuit, and the reverse summing circuit reversely propagates the current signal to the negative electrode of the memristor cross synapse array circuit. The memristive neural network is combined with associative memory, and a full connection layer of the artificial neural network is realized based on memristive, so that the network area and power consumption can be greatly reduced.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a memristive recursive neural network circuit. Background technique [0002] In recent years, a great deal of research has been done on the use of artificial neural networks (ANNs) to simulate the neural behavior of the human brain. On the one hand, many implementations of large-scale neuromorphic architectures have been proposed in order to find a model structure comparable to the computational power of the human brain. On the other hand, many studies are also trying to create intelligent agents with similar characteristics and behaviors of the human brain. [0003] Among them, the discrete Hopfield neural network is a structure of full feedback network. The characteristic of the full feedback network is that the input of any neuron is connected by the output of other neurons through a connection weight. Correspondingly, the output of any neuron is fed back to ...

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Application Information

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IPC IPC(8): G06N3/04G06N3/063
CPCG06N3/063G06N3/045
Inventor 万葛亮王雷敏万雄波
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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