CN108717570A - A kind of impulsive neural networks parameter quantification method - Google Patents
A kind of impulsive neural networks parameter quantification method Download PDFInfo
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- CN108717570A CN108717570A CN201810501442.2A CN201810501442A CN108717570A CN 108717570 A CN108717570 A CN 108717570A CN 201810501442 A CN201810501442 A CN 201810501442A CN 108717570 A CN108717570 A CN 108717570A
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- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
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- G06N3/02—Neural networks
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Abstract
The present invention relates to nerual network technique field more particularly to a kind of impulsive neural networks parameter quantification methods.The present invention method by map offline or on-line training obtain training complete original pulse neural network, the parameters such as impulsive neural networks weights, threshold value, leakage constant, set voltage, refractory period, the synaptic delay completed to training quantify, and all layers of neural network can share same group of quantization parameter or respectively one group of quantization parameter.Impulsive neural networks after parameter quantization only need a small amount of parameter that high-precision pulse neural network function can be realized.This method is high-precision simultaneously in holding, and effectively save impulsive neural networks parameter storage space improves arithmetic speed, reduces operation power consumption.
Description
Technical field
The present invention relates to nerual network technique field more particularly to a kind of impulsive neural networks parameter quantification methods.
Background technology
Impulsive neural networks (abbreviation SNN) are referred to as third generation neural network, it handles the side of information closer to human brain
Formula is the developing direction of the following nerual network technique.SNN receives information based on pulse train, has many coding modes can be with
Pulse train is construed to an actual number, common coding mode has pulse code and frequency coding.Between neuron
Communication be also to be carried out by pulse, when the film potential of a neuron is more than its threshold value, it will produce pulse letter
Number other neurons are passed to, increases or decreases its film potential.The hardware platform of SNN is referred to as neuromorphic chip or class brain core
Piece, has overturned traditional von Neumann framework completely, and this kind of chip has the features such as low-power consumption, resource consumption is few, in classification and
The performance in the classes human brain fields such as identification will be significantly better than traditional die.There are mainly two types of the training methods of SNN, one is by
The corresponding artificial neural network (abbreviation ANN) of training is mapped to trained parameter in SNN again under specified conditions, but is reflecting
It often may require that during penetrating and transmit a large amount of parameter；Another is the direct on-line study for carrying out SNN, equally also along with
Generate a large amount of parameter.Huge memory space is needed if storing parameter according to legacy memory (such as SRAM, DRAM etc.), if
Parameter is stored using new devices such as memristors, then is difficult to accurately and stably realize numerous parameters；Meanwhile huge parameter amount meeting
It reduces arithmetic speed, increase operation power consumption.There is presently no a kind of methods that can be compressed to the quantity of parameters in SNN.
Invention content
In order to solve problems in the prior art, the present invention provides a kind of methods that can reduce SNN parameter storage spaces.
Technical scheme is as follows：
A kind of impulsive neural networks parameter quantification method, which is characterized in that include the following steps：
Obtain the original SNN that training is completed.Neuron is spiking neuron (such as LIF neurons) in SNN, has input arteries and veins
Accumulation function is integrated in punching and function is provided in pulse, and for SNN using pulse train as input, the major parameter of SNN includes weights, threshold
Value, leakage constant, set voltage, refractory period, synaptic delay etc..The SNN that training is completed has the work(such as high-precision classification, identification
Energy.Obtaining the neural network that training is completed, there are mainly two types of methods：One is by map offline obtain training complete SNN,
By the methods of the training common stochastic gradient descents of ANN training ANN (including MLP, CNN, RNN, LSTM etc.), obtains satisfaction and refer to
Training process is completed after the ANN of mark requirement (such as classification, accuracy of identification etc.), then the parameter for the ANN that training is completed is mapped to
In the identical SNN of topological structure, the input of ANN is encoded into (such as pulse train of Poisson distribution) conduct afterwards using pulse train
The input of SNN, to obtain the SNN of training completion；One is the SNN completed by on-line training acquisition training, establish as certainly
The SNN or SNN of other structures is organized, the learning rules such as plasticity (STDP) are relied on using cynapse pulse sequence, using pulse sequence
It arranges (such as Poisson distribution pulse train, time encoding pulse train etc.) and SNN is trained by on-line study, adjusted in training process
The parameters such as weights, threshold value, leakage constant, set voltage, refractory period, the synaptic delay of SNN, acquisition meet index request (such as
Classification, accuracy of identification etc.) training process is completed after SNN, the weights of SNN, threshold value, leakage constant, set are fixed after training
The parameters such as voltage, refractory period, synaptic delay, to obtain the SNN of training completion；
Choose the one or more parameters for needing to quantify.The parameter that can quantify includes weights, threshold value, leakage constant, sets
Position voltage, refractory period, synaptic delay etc..
A certain layer or certain several layers of or all layer parameter distribution situation are counted respectively；
It attempts to carry out interval division to parameter.Selecting All Parameters interval division method and section number, the division methods in section
Equal point-score, non-equal point-score, confidence interval partitioning etc. can be used, the method and number of demarcation interval can be according to specific nerves
Network structure and task type and index request are attempted and are adjusted by parameter adjustment experience；
Parameter is quantified according to section.The parameter in all sections is traversed, is distributed in all in same section
Parameter is quantified as the same value (i.e. a quantized value), the size of quantized value and the specific neural network structure of positive and negative basis and appoints
Service type and index request are attempted and are adjusted by parameter adjustment experience；
It is replaced with the parameter after quantization and corresponds to parameter in original SNN, obtained parameter and quantify SNN；
Using the input of original SNN as input, parameter quantization SNN is tested, if test result meets index and wants
It asks, terminates, otherwise return to Selecting All Parameters interval division method again and section number, and interval division is carried out with after to parameter
Continuous process.
Beneficial effects of the present invention are that method of the invention can be converted into ANN SNN, and realize the parameter amount of SNN
Change, the quantization method is easy to operate flexibly to may be implemented the quantization of diversified forms, and to the performance of neural network almost without
Any influence can save storage resource, improve calculating speed.Especially when needing to realize SNN Hardwares, this method can
To reduce the consumption of the Resources on Chip such as RAM and computation complexity, hardware calculating speed and performance are improved.
Description of the drawings
Fig. 1 is a kind of SNN parameter quantification methods schematic diagram that present example provides；
Fig. 2 is one of ANN examples MLP schematic diagrames in Fig. 1；
Fig. 3 is one of ANN examples CNN schematic diagrames in Fig. 1；
Fig. 4 is one of ANN examples RNN schematic diagrames in Fig. 1；
Fig. 5 is one of ANN examples LSTM schematic diagrames in Fig. 1；
Fig. 6 is one of SNN examples self-organizing network schematic diagram in Fig. 1；
Fig. 7 is one of quantization parameter example weights distribution map and interval division situation in Fig. 1；
Fig. 8 is one of quantization parameter example threshold value distribution map and interval division situation in Fig. 2；
Fig. 9 is one of quantization parameter example leakage constant distribution map and interval division situation in Fig. 2；
Figure 10 is one of quantization parameter example set voltage distribution map and interval division situation in Fig. 2；
Figure 11 is one of quantization parameter example refractory period distribution map and interval division situation in Fig. 2；
Figure 12 is one of quantization parameter example synaptic delay distribution map and interval division situation in Fig. 2.
Figure 13 is a kind of exemplary method schematic diagram that SNN realizes parameter quantization in Fig. 1.
Specific implementation mode
The present invention is described in detail below in conjunction with the accompanying drawings, so that those skilled in the art more fully understands this hair
It is bright.Requiring particular attention is that in the following description, when perhaps known function and the detailed description of design can desalinate this
When the main contents of invention, these descriptions will be ignored herein.
As shown in Figure 1, a kind of SNN parameter quantification methods, include the following steps：
S1：Obtain the original SNN that training is completed.
Neuron is spiking neuron (such as LIF neurons) in SNN, has the function of that input pulse integrates accumulation and pulse
Provide function, SNN using pulse train as inputting, the major parameter of SNN include weights, threshold value, leakage constant, set voltage,
Refractory period, synaptic delay etc..The SNN that training is completed has the function of high-precision classification, identification etc..Obtain the nerve that training is completed
There are mainly two types of methods for network：One is the SNN that training completion is obtained by mapping offline, commonly random by training ANN
The ANN such as the methods of gradient decline training MLP (refering to Fig. 2), CNN (refering to Fig. 3), RNN (refering to Fig. 4), LSTM (refering to Fig. 5),
Acquisition completes training process after meeting the ANN of index request (such as classification, accuracy of identification etc.), then the ANN that training is completed
Parameter is mapped in the identical SNN of topological structure, by the input of ANN using pulse train coding (such as the pulse of Poisson distribution
Sequence) afterwards as the input of SNN, to obtain the SNN of training completion；One is obtain what training was completed by on-line training
SNN, establishes the SNN such as self-organizing SNN (refering to Fig. 6) or other structures, and plasticity (STDP) is relied on using cynapse pulse sequence
Equal learning rules, pass through on-line study using pulse train (such as Poisson distribution pulse train, time encoding pulse train etc.)
SNN is trained, the parameters such as weights, threshold value, leakage constant, set voltage, refractory period, the synaptic delay of SNN are adjusted in training process,
Acquisition completes training process after meeting index request (such as classification, accuracy of identification etc.) SNN, and the power of SNN is fixed after training
The parameters such as value, threshold value, leakage constant, set voltage, refractory period, synaptic delay, to obtain the SNN of training completion.
S2：Choose the one or more parameters for needing to quantify.
A parameter can once be quantified, can also once quantify multiple parameters.The parameter that can quantify includes weights, threshold
Value, leakage constant, set voltage, refractory period, synaptic delay etc..
S3：A certain layer or certain several layers of or all layer parameter distribution situation are counted respectively.
For the parameter that some needs quantifies, counts a certain layers of SNN or certain several layers of or all layers parameter and draw ginseng
Number distribution map.
S4：It attempts to carry out interval division to parameter.
The division methods of Selecting All Parameters interval division method and section number, section can be used equal point-score, non-equal point-score, set
Believe interval division method etc., the method and number of demarcation interval according to specific neural network structure and task type and can refer to
Mark requires, and is attempted and is adjusted by parameter adjustment experience.Described in S3 on the basis of parameter distribution figure, using equal point-score to power
It is worth (refering to Fig. 7), threshold value (refering to Fig. 8), leakage constant (refering to Fig. 9), set voltage (refering to fig. 1 0), refractory period (refering to figure
11), the result after the parameters such as synaptic delay (refering to fig. 1 2) progress interval division provides in the accompanying drawings.
S5：Parameter is quantified according to section.
The parameter in all sections is traversed, all parameters being distributed in same section are quantified as the same value (i.e. one
A quantized value), the size of quantized value and the specific neural network structure of positive and negative basis and task type and index request pass through
Parameter adjustment experience is attempted and is adjusted.
S6：It obtains parameter and quantifies SNN.
It is replaced with the parameter after quantization and corresponds to parameter in original SNN, obtained parameter and quantify SNN.
S7：Test parameter quantifies SNN.
Using the input of original SNN as input, parameter quantization SNN is tested, if test result meets index and wants
It asks, terminates, otherwise return to Selecting All Parameters interval division method again and section number, and interval division is carried out with after to parameter
Continuous process.
Refering to fig. 13, the quantization method is carried out for realizing MNIST Handwritten Digit Recognition tasks using MLP below
Further explanation includes the following steps：
S8：Set target identification accuracy.
That is index request described in SI, target identification accuracy of the setting nerve network system to MNIST test sets.
S9：MLP is trained using BP algorithm and trained weights are mapped directly into SNN.
That is off-line training method described in S1 needs to meet some specific conditions when training MLP in this example：(1)
All units of MLP all should use Relu functions as activation primitive；(2) in the training process, the deviation of neuron is fixed
It is 0.
S10：The threshold value and maximum frequency of SNN are set.
This is the peculiar parameter of SNN.The input of SNN needs to change into impulse form, so needing to carry out input picture
Coding, in this example encodes each pixel of picture using the mode of Poisson distribution frequency coding, the frequency of pulse
It is proportionate with the size of input pixel.The maximum frequency of pulse and the threshold value of LIF neurons according to the sizes of mapping parameters and
The discrimination of subsequent feedback is attempted and is adjusted by parameter adjustment experience.
S11：Test SNN discriminations.
It carries out in next step, otherwise returning to S8 re -trainings MLP and follow-up mistake if meeting target identification accuracy described in S8
Journey.
S12：Obtain all layers of weights distribution map and the respectively section of weights.
That is it attempts to carry out interval division to parameter described in all layer parameter distribution situations of statistics and S4 described in S3.In this example
Statistics and interval division only are carried out to weights, attempt to be divided using equal point-score, section number is 4.
S13：Weights are quantified and (attempt to be quantified using interval midpoint) according to section.
That is parameter is quantified according to section described in S5.It attempts to be quantified using interval midpoint in this example.
S14：Traversal maximizing wmax and minimum value wmin is carried out to all weights of SNN, takes putting down for wmax and wmin
Mean value is denoted as w0.
The step of being quantified using interval midpoint.
S15：The average value of wmin and w0 is taken to be denoted as w-1 again；The average value of wmax and w0 is taken to be denoted as w1.
The step of being quantified using interval midpoint.
S16：All weights are traversed again, if weights between wmin and w-1, enable weights be equal to the flat of the two
Mean value x1；If between w-1 and w0, enables weights be equal to the average value x2 of the two, similarly obtain x3 and x4.
The step of being quantified using interval midpoint.
S17：Test SNN discriminations.
That is test parameter described in S7 quantifies SNN.Terminate if meeting performance indicator, otherwise returns to S12 and choose area again
Between division methods and subsequent process.
Claims (4)
1. a kind of impulsive neural networks parameter quantification method, which is characterized in that include the following steps：
S1, the original pulse neural network that training is completed is obtained, the parameter of original pulse neural network includes weights, threshold value, lets out
Leak constant, set voltage, refractory period, synaptic delay；
S2, one or more parameter for needing to quantify is chosen；
Distribution situation of the parameter in neural network selected by S3, statistics；
S4, interval division is carried out to selected parameter；
S5, parameter is quantified according to section, that is, will be distributed over the parameter in same section and is quantified as the same value；
S6, parameter quantification impulse neural network is obtained, that is, the quantized value obtained in step 5 is used to substitute original pulse neural network
It can only strange corresponding initial parameter；
S7, the step S6 parameter quantification impulse neural networks obtained are tested：Using the defeated of primitivation impulsive neural networks
Enter as input, parameter quantization neural network is tested, meets if desired indicator requires if test result and terminate, otherwise return
Return step S3.
2. a kind of impulsive neural networks parameter quantification method according to claim 1, which is characterized in that described in step S1
Obtaining the specific method of original pulse neural network that training is completed is：
By the corresponding artificial neural network of training, the artificial neural network is multilayer perceptron, convolutional neural networks, cycle
One kind in neural network, shot and long term memory network, then training parameter is mapped to the identical impulsive neural networks of topological structure,
Input data is encoded using pulse train, obtains original pulse neural network；
Alternatively,
Impulsive neural networks are established, using pulse train as inputting, the learning machine of plasticity is relied on using cynapse pulse sequence
On-line training impulsive neural networks processed, the parameter of fixed pulse neural network after training, to obtain the original of training completion
Initial pulse neural network.
3. a kind of impulsive neural networks parameter quantification method according to claim 2, which is characterized in that the step S3's
Specific method is：
Parameter distribution situation of the selected parameter of statistics in a certain layer network；
Alternatively,
The parameter that Selecting All Parameters are counted in a few layer networks distinguishes situation；
Again alternatively,
The parameter that Selecting All Parameters are counted in each layer network distinguishes situation.
4. a kind of impulsive neural networks parameter quantification method according to claim 3, which is characterized in that the step S4's
Specific method is：
Parameter section is divided using a kind of in equal point-score, non-equal point-score, confidence interval partitioning, and obtains interval number
Mesh.
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