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Spiking neural network neuron circuit based on LIF model

A spiking neural network and neuron technology, applied in biological neural network model, neural architecture, physical implementation, etc., can solve problems such as poor work performance, achieve accurate comparison results, save power consumption, and adjust the current leakage rate.

Active Publication Date: 2021-03-09
CHONGQING UNIV OF POSTS & TELECOMM
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method for realizing a pulse neural network neuron circuit based on the LIF model, so as to overcome the problem of poor working performance of the electronic system under low power supply voltage in the prior art, and on the basis of the typical LIF model, make the neuron circuit With a refractory period, adjust the refractory period by adjusting the bias voltage, thereby controlling the pulse transmission frequency of the neuron circuit, so that the neuron circuit can adapt to working conditions at various frequencies

Method used

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  • Spiking neural network neuron circuit based on LIF model
  • Spiking neural network neuron circuit based on LIF model
  • Spiking neural network neuron circuit based on LIF model

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Embodiment

[0035] Such as figure 1 As shown, according to one aspect of the present invention, a neuron circuit based on the LIF model is provided, including: a membrane potential accumulation circuit, a leakage circuit, a pulse generation circuit, a refractory period circuit and a reset circuit; the membrane potential accumulation circuit is used When an input signal is input, the charge is accumulated to control the increase of the membrane voltage; the leakage circuit is used to leak the membrane voltage when no input signal is input, to the resting potential or to the arrival of the next input signal; The pulse generation circuit is used to receive the membrane voltage and realize the activation condition of the neuron circuit according to the membrane voltage state; the refractory period circuit is used to control the refractory period cycle through the bias voltage and generate a reset signal V rst Applied to the reset circuit; the reset circuit is used to control the reset of the ...

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Abstract

The invention relates to a pulse neural network neuron circuit based on an LIF (Leaky Integrate and Fire) model, and belongs to the field of integrated circuit design. The circuit mainly comprises a membrane potential accumulation circuit, a leakage circuit, a pulse generation circuit, a no-response period circuit and a reset circuit. According to the invention, the integral leakage ignition function of the LIF neuron model is realized, the stress tolerance characteristic of biological neurons is simulated, and the stress tolerance is adjustable. Based on the CMOS technology, the circuit structure is simple, and a sub-threshold region MOS tube is mostly adopted in the circuit, so that the neuron circuit has the characteristic of ultra-low power consumption; the input current of the circuitis pA level, the output pulse frequency of the circuit is Hz level, the typical working state of biological neurons can be well simulated, the functions of the neurons are effectively realized, and the circuit can be used for realizing a large-scale pulse neural network system.

Description

technical field [0001] The invention belongs to the technical field of integrated circuit design, and in particular relates to a method for realizing a pulse neural network neuron circuit based on a LIF (Leaky Integrate and Fire) model. Background technique [0002] Biological systems are very energy efficient, especially the brain, which consists of billions of neuron cells and consumes about 20W. These units are noisy, imprecise, unreliable analog devices. When integrated into brain structures composed of interacting neurons, they can solve complex tasks and exhibit complex behaviors in real time and with high precision at very low power consumption. There are 1011 neurons and 1015 synapses in the human brain and the energy consumption is extremely low. If you want to simulate the parallel processing of information by a large number of neurons and synapses in the brain, the circuit needs to have ultra-low power performance and occupy as small an area as possible. The di...

Claims

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Application Information

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IPC IPC(8): G06N3/063G06N3/04
CPCG06N3/063G06N3/049
Inventor 王巍张珊赵汝法张定冬张涛洪刘博文袁军
Owner CHONGQING UNIV OF POSTS & TELECOMM
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