Probabilistic neuron circuit, and probabilistic neural network topological structure and application thereof

A probabilistic neural network and topological structure technology, applied in the field of microelectronic devices, can solve the problems of limited application of neuron circuits and achieve the effect of improving the scope of application

Active Publication Date: 2020-04-10
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The present invention provides a probabilistic neuron circuit, a probabilistic neural network topology and its application, which are use...

Method used

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  • Probabilistic neuron circuit, and probabilistic neural network topological structure and application thereof
  • Probabilistic neuron circuit, and probabilistic neural network topological structure and application thereof
  • Probabilistic neuron circuit, and probabilistic neural network topological structure and application thereof

Examples

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Effect test

Embodiment 1

[0044] A probabilistic neuron circuit 100, such as figure 1 As shown, including: integrating capacitor, non-fixed-threshold volatile device and load resistor; one end of the integrating capacitor is grounded, the other end is externally connected to a synaptic resistor to external signal input source and one end connected to a non-fixed-threshold volatile device, non-fixed The other end of the threshold volatile device is connected to one end of the load resistor, and the other end of the load resistor is connected to ground.

[0045] Due to the characteristics of the non-fixed threshold volatile device itself, its excitation (turn-on) voltage threshold is not fixed, it is a randomly changing value, the probability corresponding to each excitation voltage is generally different, and the variation law roughly satisfies the Gaussian distribution, for example, figure 2 As shown, the voltage required for the volatile memory device to change from a high-resistance state to a low-r...

Embodiment 2

[0049] A probabilistic neural network topology 200, such as image 3 As shown, it includes: a plurality of input neuron circuits, a plurality of output neuron circuits, a lateral inhibitory neuron circuit, and a signal processor; wherein, the output neuron circuit is a probabilistic neuron circuit as described above;

[0050] Each input neuron circuit is used to send discharge signals to each probability neuron circuit; each probability neuron circuit is used to perform random excitation based on its non-fixed excitation threshold and the electrical signal sent by each input neuron circuit; lateral inhibition The neuron circuit is used to suppress the subsequent excitation of other probability neuron circuits when receiving the signals excited by the first n probability neuron circuits, where n≥1; the signal processor is used to collect whether each probability neuron circuit is excited or not signal and perform signal processing.

[0051] A plurality of input neuron circuits...

Embodiment 3

[0060] An application of any probabilistic neural network topology described in the second embodiment above is applied to the determination of the probability of non-deterministic problems.

[0061] It should be noted that the non-deterministic problem is Uncertainty Quantification.

[0062] Based on the foregoing, the excitation threshold of non-fixed-threshold volatile devices is not fixed and the excitation is random, so the excitation is uncertain. Excited first, this is a probabilistic event. Therefore, using non-fixed threshold volatile devices for the probability determination of non-deterministic problems, using hardware with natural attributes, the generated probability has real probability attributes. At this stage, computers Among them, the method of generating probability is to generate a random number, and then realize it by mathematical algorithm, and this random number does not really appear randomly, it is a series of numbers obtained according to a certain cal...

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Abstract

The invention discloses a probabilistic neuron circuit, and a probabilistic neural network topological structure and application thereof. The probabilistic neuron circuit comprises an integrating capacitor, a non-fixed threshold volatile device and a load resistor, wherein one end of the integrating capacitor is externally connected with a synaptic resistor and connected with one end of the non-fixed threshold volatile device, and the other end of the volatile device is connected with one end of the load resistor. The network topological structure comprises a plurality of input neuron circuits, a plurality of probabilistic neuron circuits and a lateral suppression neuron circuit, wherein each probabilistic neuron circuit is used for carrying out random excitation based on the non-fixed excitation threshold value of the probabilistic neuron circuit and an electric signal emitted by each input neuron circuit; and the suppression neuron circuit is used for suppressing the excitation of other subsequent probabilistic neuron circuits when receiving signals excited by the first n probability neuron circuits. According to the invention, the non-fixed threshold volatile device is introduced into the neuron circuit, so the application of the neuron circuit is greatly expanded, and the neuron circuit can be particularly used for solving non-deterministic problems and has a reliable solution result.

Description

technical field [0001] The invention belongs to the technical field of microelectronic devices, and more specifically relates to a probabilistic neuron circuit, a probabilistic neural network topology and applications thereof. Background technique [0002] By simulating the learning principle of the human brain, brain-like computing has the characteristics of high speed, low power consumption and self-learning. It is a strong competitor to replace the current von Neumann computing architecture. The core mechanism of brain-inspired computing is to simulate the human brain to send out pulses through the excitation of neurons to complete the transmission of information, and then adjust the synaptic connection weights between the front and back neurons to complete the learning function. The network with pulse as the information transmission carrier is called pulse neural network. At the hardware level, the functions of neurons and synapses are simulated by microelectronic device...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/063G06N3/08
CPCG06N3/049G06N3/063G06N3/08
Inventor 童浩胡庆王宽何毓辉缪向水
Owner HUAZHONG UNIV OF SCI & TECH
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