CN109472348B - LSTM neural network system based on memristor cross array - Google Patents
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Abstract
The invention discloses an LSTM neural network system based on a memristor cross array, which comprises: input layer, characteristic extraction layer and categorised output layer, the characteristic extraction layer includes: data memory, first memristor cross array, first DA converter and AD converter, the categorised output layer includes: the second DA converter, the second memristor cross array and the voltage comparator; the first memristor cross array is used for carrying out feature extraction; the second memristor cross array is used for carrying out feature classification, and the voltage comparator is used for comparing a plurality of analog voltages to obtain a comparison result of the plurality of analog voltages; and taking the maximum value in the comparison results as the classification result of the digital signals of the input layer. The system has smaller size and lower power consumption.
Description
Technical Field
The invention belongs to the field of neural networks, and particularly relates to an LSTM neural network system based on a memristor cross array.
Background
The development of deep neural networks such as Convolutional Neural Networks (CNN), Full Convolutional Networks (FCN), and long term short term memory (LSTM) has facilitated the study of deep neural network hardware design. In the field of deep neural network hardware circuit design, due to the reasons of a large amount of input data (such as images or texts), complex network structure, numerous network parameters and the like, a common CMOS design deep neural network has the problems of too large circuit size, high power consumption and the like.
In 2008, researchers in hewlett packard company made nano-sized memristors for the first time, the basic challenge of reality of a neuromorphic computing system was the development of artificial synapses, and the discovery of memristors provided perfectly-fitted elements for our design of neuromorphic computing architectures. Memristors have shown a wide application prospect in neural network hardware design due to their excellent properties of nanoscale size, nonvolatility, high density, low power consumption, compatibility with CMOS technology, and the like.
However, the LSTM neural network has great difference from CNN in structure and operation principle, so the whole hardware circuit structure based on the convolutional neural network is no longer practical in LSTM, and meanwhile, the prior art has the technical problems of large size and large power consumption.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides an LSTM neural network system based on a memristor cross array, so that the technical problems of large size and large power consumption in the prior art are solved.
In order to achieve the above object, the present invention provides an LSTM neural network system based on a memristive crossbar array, including: an input layer, a feature extraction layer and a classification output layer,
the feature extraction layer includes: the device comprises a data memory, a first memristor cross array, a first DA converter and an AD converter, wherein the data memory is used for storing a digital signal of an input layer and a digital signal after feature extraction; the first DA converter is used for converting the digital signal of the input layer into a first analog voltage; the first memristor cross array is used for carrying out feature extraction on the first analog voltage to obtain a first current; the AD converter is used for converting the first current into a digital signal after characteristic extraction;
the classification output layer includes: the second DA converter, the second memristor cross array and the voltage comparator; the second DA converter is used for converting the digital signal after the characteristic extraction into a second analog voltage; the second memristor cross array is used for carrying out feature classification on the second analog voltage to obtain a plurality of analog voltages; the voltage comparator is used for comparing the plurality of analog voltages to obtain a comparison result of the plurality of analog voltages; and taking the maximum value in the comparison results as the classification result of the digital signals of the input layer.
Further, the first memristive crossbar array includes a voltage input port, a threshold memristor, a voltage inverter, an operational amplifier, and a multiplier,
in any two adjacent voltage input ports, one voltage input port is connected with the threshold memristor through a voltage inverter, the other voltage input port is directly connected with the threshold memristor, and the operational amplifier is connected with the threshold memristor in parallel; one end of the voltage phase inverter is connected with the output end of the operational amplifier, and the other end of the voltage phase inverter is connected with the input end of the multiplier.
Further, the operational amplifier is connected in parallel with the threshold memristor and is used for realizing the operational function of the Sigmoid activation function and converting the current signal into the voltage signal.
Further, the operational amplifier is connected in parallel with the threshold memristor and is used for achieving the operational function of the hyperbolic tangent activation function and converting the current signal into the voltage signal.
Further, the second memristor cross array comprises voltage input ports, threshold memristors and voltage inverters, wherein one of any two adjacent voltage input ports is connected with the threshold memristor through the voltage inverter, and the other voltage input port is directly connected with the threshold memristor.
Further, to avoid threshold memristor shorts, no electrical connection is made at the intersection of the first memristive crossbar array and no electrical connection is made at the intersection of the second memristive crossbar array.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) due to the nanoscale size of the memristor and the high density of the cross array, the hardware circuit is small in size, easy to integrate and suitable for being popularized in deep neural network hardware design in a large range. In addition, the low power consumption characteristic of the memristor enables the power consumption of the whole system to be obviously reduced compared with the power consumption of materials such as CMOS.
(2) Usually, one threshold memristor can only express one positive weight, and in any adjacent voltage input ports, one voltage input port is connected with the threshold memristor through a voltage inverter, and the other voltage input port is directly connected with the threshold memristor, so that two adjacent threshold memristors can express one weight with positive and negative weights.
Drawings
FIG. 1 is a schematic diagram of a long-short term memory neural network provided by an embodiment of the present invention;
FIG. 2 is a structural diagram of an LSTM neural network system based on a memristor crossbar array according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a feature extraction layer unit of the neural network with long and short term memory according to an embodiment of the present invention;
FIG. 4 is a circuit diagram of a first memristive crossbar array provided by an embodiment of the present disclosure;
FIG. 5 is a circuit diagram of a second memristive crossbar array provided by an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, a long term short term memory (LSTM) neural network includes: the device comprises an input layer, a feature extraction layer and a classification output layer. As shown in fig. 2, an LSTM neural network system based on a memristive crossbar array includes: input layer, characteristic extraction layer and categorised output layer, the characteristic extraction layer includes: the data memory, first switch, first reading circuit, write circuit, first memristor crossbar array, first DA converter and AD converter, categorised output layer includes: the first switch, the first reading circuit, the first DA converter, the first memristor crossbar array and the voltage comparator are connected in series; when the first switch is closed and the second switch is opened, the system performs feature extraction; when the first switch is switched off and the second switch is switched off, the system carries out classified output;
the data memory is used for storing the digital signal of the input layer and the digital signal after the characteristic extraction;
the first reading circuit is used for reading the digital signals of the input layer in the data memory and transmitting the digital signals to the first DA converter;
the first DA converter is used for converting the digital signal of the input layer into a first analog voltage;
the first memristor cross array is used for performing feature extraction on the first analog voltage to obtain a first current;
the AD converter is used for converting the first current into a digital signal after feature extraction;
the writing circuit is used for writing the digital signal after the characteristic extraction into the data memory;
the second reading circuit is used for reading the digital signals after the features in the data memory are extracted and transmitting the digital signals to the second DA converter;
the second DA converter is used for converting the digital signal after the characteristic extraction into a second analog voltage;
the second memristor cross array is used for carrying out feature classification on second analog voltages to obtain a plurality of analog voltages;
the voltage comparator is used for comparing the plurality of analog voltages to obtain a comparison result of the plurality of analog voltages;
and taking the maximum value in the comparison results as the classification result of the digital signals of the input layer.
FIG. 3 shows a schematic diagram of an internal cell (LSTM cell) of the LSTM neural network, in which ctIs the state of the LSTM cell, ct-1Is the state of the LSTM cell at the last time, htIs the output of the LSTM cell, ht-1Is the output of the last time LSTM cell. c. Ct-1Has a data width of 1, ctHas a data width of 64, Sigmoid represents a Sigmoid activation function, Tanh represents a hyperbolic tangent activation function, f represents the output of a forgetting gate, i represents an input gate, j represents an update gate, o represents an output gate, f _ b represents a forgetting offset, b represents a hysteresis offset, andtindicating the offset of a forgetting gate, biIndicating the offset of the input gate, bjIndicating the offset of the update gate, boIndicating the offset of the output gate, WhfStatus weight, W, representing a forgotten doorxfInput weight, W, representing forgetting gatehiIndication inputWeight of state of entry, WxiRepresenting the input weight, W, of the input gatehjIndicating the status weight, W, of the update gatexjInput weight, W, representing an update gatehoRepresenting the state weight of the output gate, WxoRepresenting the input weights of the output gates, and X represents the input of the LSTM cell.
The feature extraction part in the schematic diagram of the LSTM cell is converted into a mathematical expression as follows:
f＝sigmoid(x*Wxf+ht-1*Whf+bf+f_b)
i＝sigmoid(x*Wxi+ht-1*Whi+bi)
j＝tanh(x*Wxj+ht-1*Whj+bj)
o＝sigmoid(x*Wxo+ht-1*Who+bo)
the above data calculates the function of the corresponding crossbar array, and the following is a mathematical expression of the completed function of the following auxiliary circuits of the crossbar array:
ct＝ct-1*f+i*j
ht＝o*tanh(ct)
the data in the above formula is in the form of analog voltages in the circuit.
As shown in fig. 4, the first memristive crossbar array includes a first trunk circuit including a voltage input port 1, a threshold memristor 2, a voltage inverter 3, an operational amplifier 4, and a first auxiliary circuit including an operational amplifier 4, a resistor 5, and a multiplier 6, and no electrical connection is made at the intersection of the first memristive crossbar array.
An LSTM neural network system based on a memristive cross array comprises n LSTM cells, namely the LSTM neural network system based on the memristive cross array comprises n first memristive cross arrays, and the first analog voltage comprises VX1-VXmAnd Vh1-VhnThe first analog voltage is input to the first trunk circuit through the voltage input port. One threshold memristor can only express one positive weight, and any adjacent memristors in the inventionIn the voltage input ports, one voltage input port is connected with the threshold memristor through the voltage inverter, and the other voltage input port is directly connected with the threshold memristor, so that two adjacent threshold memristors can express a weight with positive and negative values.
M1 and M2 are both threshold memristors, Vs1 +Is a 1V DC voltage, Vs1 -Ground, Vs2 +Is a 1V DC voltage, Vs2 -Is a direct current voltage of-1V, and a resistor R1、R2、R3、R4And R5The medium resistance values need to be the same, and 1K ohm to 10K ohm can be adopted. The operational amplifier is connected with the threshold memristor M1 in parallel to realize a Sigmoid activation function and convert the current signal into a voltage signal, and the operational amplifier is connected with the threshold memristor M2 in parallel to realize a hyperbolic tangent activation function and convert the current signal into a voltage signal. One end of the voltage phase inverter is connected with the output end of the operational amplifier, and the other end of the voltage phase inverter is connected with the input end of the multiplier and used for converting the direction of the voltage.
Vc(t-1)Representing the state of the first memristive crossbar array at the last moment, VctRepresenting the state of the first memristive crossbar array, VhtRepresenting a first current. Mathematical expression of the completion function of the first auxiliary circuit:
ct＝ct-1*f+i*j
ht＝o*tanh(ct)
as shown in fig. 5, the second memristive crossbar array includes a second trunk circuit and a second auxiliary circuit, the second trunk circuit includes voltage input ports, threshold memristors, and voltage inverters, one voltage input port is connected to the threshold memristor through the voltage inverter among any adjacent voltage input ports, and the other voltage input port is directly connected to the threshold memristor. The intersection point of the second memristor cross array is not electrically connected, the second auxiliary circuit is a voltage comparator, and the voltage comparator is used for comparing a plurality of analog voltages to obtain a comparison result of the plurality of analog voltages; using the maximum value of the comparison results as the classification result V of the digital signal of the input layero。
The invention solves the difficult problem of hardware design of the LSTM neural network, provides a hardware implementation method including an activation function while introducing a memristor to carry out circuit design, and provides a complete implementation scheme of the LSTM neural network based on a memristor cross array. Due to the nanoscale size of the memristor and the high density of the cross array, the hardware circuit of the design is small in size, easy to integrate and more suitable for being popularized in deep neural network hardware design in a large range. In addition, the low power consumption characteristic of the memristor enables the power consumption of the whole system to be obviously reduced compared with the power consumption of materials such as CMOS.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (3)
1. An LSTM neural network system based on a memristive crossbar array, comprising: an input layer, a feature extraction layer and a classification output layer, which are characterized in that,
the feature extraction layer includes: the device comprises a data memory, a first memristor cross array, a first DA converter and an AD converter, wherein the data memory is used for storing a digital signal of an input layer and a digital signal after feature extraction; the first DA converter is used for converting the digital signal of the input layer into a first analog voltage; the first memristor cross array is used for carrying out feature extraction on the first analog voltage to obtain a first current; the AD converter is used for converting the first current into a digital signal after characteristic extraction;
the classification output layer includes: the second DA converter, the second memristor cross array and the voltage comparator; the second DA converter is used for converting the digital signal after the characteristic extraction into a second analog voltage; the second memristor cross array is used for carrying out feature classification on the second analog voltage to obtain a plurality of analog voltages; the voltage comparator is used for comparing the plurality of analog voltages to obtain a comparison result of the plurality of analog voltages; taking the maximum value in the comparison results as the classification result of the digital signals of the input layer;
the first memristive crossbar array includes a voltage input port, two threshold memristors, a voltage inverter, an operational amplifier, and a multiplier,
in any two adjacent voltage input ports, one voltage input port is connected with the threshold memristor through the voltage inverter, and the other voltage input port is directly connected with the threshold memristor, so that the two adjacent threshold memristors can express a weight with positive and negative values; the operational amplifier is connected with one threshold memristor in parallel and used for achieving the operational function of a Sigmoid activation function and converting a current signal into a voltage signal, and the operational amplifier is connected with the other threshold memristor in parallel and used for achieving the operational function of a hyperbolic tangent activation function and converting the current signal into the voltage signal; one end of the voltage phase inverter is connected with the output end of the operational amplifier, and the other end of the voltage phase inverter is connected with the input end of the multiplier and used for converting the direction of the voltage.
2. The LSTM neural network system based on a memristive crossbar array, according to claim 1, wherein the second memristive crossbar array comprises a voltage input port, a threshold memristor and a voltage inverter, one voltage input port of any two adjacent voltage input ports is connected with the threshold memristor through the voltage inverter, and the other voltage input port is directly connected with the threshold memristor.
3. An LSTM neural network system based on memristive crossbar arrays, according to claim 1, wherein the intersections of the first memristive crossbar array are not electrically connected, and the intersections of the second memristive crossbar array are not electrically connected.
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| CN111460365B (en) * | 2020-03-10 | 2021-12-03 | 华中科技大学 | Equation set solver based on memristive linear neural network and operation method thereof |
| CN111680792A (en) * | 2020-06-18 | 2020-09-18 | 中国人民解放军国防科技大学 | Activation function circuit, memristor neural network and control method of memristor neural network |
| CN112884141B (en) * | 2021-04-16 | 2022-10-21 | 安徽大学 | Memristive coupling Hindmarsh-Rose neuron circuit |
| CN113988281A (en) * | 2021-10-26 | 2022-01-28 | 重庆因普乐科技有限公司 | Long-short time memory network implementation method based on memristor structure |
| CN116523011B (en) * | 2023-07-03 | 2023-09-15 | 中国人民解放军国防科技大学 | Memristor-based binary neural network layer circuit and binary neural network training method |
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| CN106815636A (en) * | 2016-12-30 | 2017-06-09 | 华中科技大学 | A kind of neuron circuit based on memristor |
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