Programmable threshold value circuit

A circuit and threshold technology, applied in the field of integrated circuits and neural networks, can solve the problems of a large number of tubes, inconvenient integration, high power consumption, etc., and achieve the effects of low cost, simple structure and low power consumption

Active Publication Date: 2013-09-25
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: aiming at the defects of large number of tubes, high cost, high power consumption, and inconvenient integration of the circuits used in the neural network in the traditional technology, a simple structure, low cost, and low power consumption are proposed. , Programmable threshold circuit for easy integration, can be applied in artificial neural network

Method used

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Examples

Experimental program
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Effect test

Embodiment 1

[0034] Such as Figure 4 As shown, in this example, the neuron field effect transistor is an N-type neuron field effect transistor, its drain terminal is connected to the voltage source VDD, and the source terminal is grounded through the resistor R6; each weight adjustment circuit unit is composed of a memristor The top electrode of the memristor is connected to the input signal, and the bottom electrode is connected to the control grid of the neuron field effect transistor to control the voltage of the control grid, and the bottom electrode is grounded through the resistor; the output signal of the whole circuit is controlled by the neuron The source terminal voltage of the field effect tube is drawn out.

[0035] From Figure 4 It can be seen that the weight adjustment circuit unit composed of memristor M1 and resistor R1 is connected to the first control gate of the neuron field effect transistor, and M1 is connected to the input signal V1; the circuit unit composed of me...

Embodiment 2

[0053] Such as Figure 9 As shown, the programmable threshold circuit structure in this example is the same as Figure 4 The structures in are similar, and the weight adjustment circuit units are composed of memristors and resistors. The difference is that the positions of the memristors and resistors in this example are equivalent to those in Figure 4 The neuron FET is still an N-type neuron FET, its drain terminal is connected to the voltage source VDD, and the source terminal is grounded through the resistor R6; each weight adjustment circuit unit is composed of a memristor One end of the resistance is connected to the input signal, the other end is connected to the control grid, the top electrode of the memristor is connected to the control grid, and the bottom electrode is grounded.

[0054] From Figure 9 It can be seen that the weight adjustment circuit unit composed of memristor M1 and resistor R1 is connected to the first control gate of the neuron field effect tra...

Embodiment 3

[0056] Such as Figure 10 As shown, the programmable threshold circuit structure in this example is the same as Figure 4 The structure in is similar, the difference is that the Figure 4 The resistors in the weight adjustment circuit unit are all replaced by memristors, that is, the weight adjustment circuit unit in this example is composed of the first memristor and the second memristor; in this example, the neuron field effect transistor It is still an N-type neuron field effect transistor, its drain terminal is connected to a voltage source VDD, and its source terminal is grounded through a resistor R1.

[0057] From Figure 10 It can be seen that the weight adjustment circuit unit composed of memristor M1 and memristor M6 is connected to the first control gate of the neuron field effect transistor, and M1 is connected to the input signal V1; the memristor M2 and the memristor The weight adjustment circuit unit composed of M7 is connected to the second control grid of the...

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PUM

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Abstract

The invention relates to the field of integrated circuits and neural networks, and provides a programmable threshold value circuit which is simple in structure, low in cost, little in power consumption and convenient to integrate to overcome the defects that in the prior art, circuits applied to the neural networks are large in the number of tubes, high in cost, high in power consumption and not convenient to integrate. The programmable threshold value circuit comprises a nerve cell field effect tube, the nerve cell field effect tube comprises a plurality of control grids and further comprises weight adjusting circuit units, the number of the weight adjusting circuit units is the same as the number of the control grids of the nerve cell field effect tube, the weight adjusting circuit units are connected with the control grids one to one, the weight adjusting circuit units are used for adjusting synaptic weights of the nerve cell field effect tube, and the nerve cell field effect tube is an N type nerve cell field effect tube or a P type nerve cell field effect tube. The programmable threshold value circuit is suitable for artificial neural networks.

Description

technical field [0001] The present invention relates to the field of integrated circuits and neural networks, and in particular to a programmable threshold circuit, which is used to simulate the weight adjustment function of time-series plasticity of neurons, and is also used for the two working states of neurons (learning state, calculation state) simulation. Background technique [0002] As digital computers encounter insoluble difficulties in fuzzy pattern recognition, associative memory and self-learning, neural network computing has regained people's attention, and artificial neural networks have emerged as the times require. An artificial neural network refers to a computing system that operates in a manner similar to a biological brain, based on an electronic system. Artificial neural networks create connections between processing units that are essentially equivalent in function to the neurons of a biological brain. Therefore, the foundation of the neural network i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/06
Inventor 刘洋胡绍刚徐艳飞董华吴霜毅于奇
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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