Patent Publication Number: US-10762426-B2

Title: Multi-iteration compression for deep neural networks

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to Chinese Patent Application Number 201611105480.3 filed on Dec. 5, 2016, Chinese Patent Application Number 201610663201.9 filed on Aug. 12, 2016 and Chinese Patent Application Number 201610663563.8 filed on Aug. 12, 2016, Chinese Patent Application Number 201610663175.X filed on Aug. 12, 2016, U.S. application Ser. No. 15/242,622 filed on Aug. 22, 2016, U.S. application Ser. No. 15/242,624 filed on Aug. 22, 2016, U.S. application Ser. No. 15/242,625 filed on Aug. 22, 2016, the entire contents of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present invention relates to a multi-iteration compression method for deep neural networks and the device thereof In particular, the present invention relates to how to compress dense neural networks into sparse neural networks without degrading the accuracy of the neural networks. 
     BACKGROUND ART 
     Compression of Artificial Neural Networks 
     Artificial Neural Networks (ANNs), also called NNs, are a distributed parallel information processing models which imitate behavioral characteristics of animal neural networks. In recent years, studies of ANNs have achieved rapid developments, and ANNs have been widely applied in various fields, such as image recognition, speech recognition, natural language processing, weather forecasting, gene expression, contents pushing, etc. 
     In neural networks, there exists a large number of nodes (also called neurons) which are connected to each other. Neural networks have two features: 1) Each neuron calculates the weighted input values from other adjacent neurons via certain output function (also called Activation Function); 2) The information transmission intensity between neurons is measured by so-called weights, and such weights might be adjusted by self-learning of certain algorithms. 
     Early neural networks have only two layers: the input layer and the output layer. Thus, these neural networks cannot process complex logic, limiting their practical use. 
     As shown in  FIG. 1 , Deep Neural Networks (DNNs) have revolutionarily addressed such defect by adding a hidden intermediate layer between the input layer and the output layer 
     Moreover, Recurrent Neural Networks (RNNs) are commonly used DNN models, which differ from conventional Feed-forward Neural Networks in that RNNs have introduced oriented loop and are capable of processing forward-backward correlations between inputs. In particular, in speech recognition, there are strong forward-backward correlations between input signals. For example, one word is closely related to its preceding word in a series of voice signals. Thus, RNNs has been widely applied in speech recognition domain. 
     However, the scale of neural networks is exploding due to rapid developments in recent years. Some of the advanced neural network models might have hundreds of layers and billions of connections, and the implementation thereof is both calculation-centric and memory-centric. Since neural networks are becoming larger, it is critical to compress neural network models into smaller scale. 
     For example, in DNNs, connection relations between neurons can be expressed mathematically as a series of matrices. Although a well-trained neural network is accurate in prediction, its matrices are dense matrices. That is, the matrices are filled with non-zero elements, consuming extensive storage resources and computation resources, which reduces computational speed and increases costs. Thus, it faces huge challenges in deploying DNNs in mobile terminals, significantly restricting practical use and development of neural networks. 
       FIG. 2  shows a compression method which was proposed by one of the inventors in earlier works. 
     As shown in  FIG. 2 , the compression method comprises learning, pruning, and training the neural network. In the first step, it learns which connection is important by training connectivity. The second step is to prune the low-weight connections. In the third step, it retrains the neural networks by fine-tuning the weights of neural network. In recent years, studies show that in the matrix of a trained neural network model, elements with larger weights represent important connections, while other elements with smaller weights have relatively small impact and can be removed (e.g., set to zero). Thus, low-weight connections are pruned, converting a dense network into a sparse network. 
       FIG. 3  shows synapses and neurons before and after pruning according to the method proposed in  FIG. 2 . 
     The final step of  FIG. 2  involves retraining the sparse network to learn the final weights for the remaining sparse connections. By retraining the sparse network, the remaining weights in the matrix can be adjusted, ensuring that the accuracy of the network will not be compromised. 
     By compressing a dense neural network into a sparse neural network, the computation amount and storage amount can be effectively reduced, achieving acceleration of running an ANN while maintaining its accuracy. Compression of neural network models are especially important for specialized sparse neural network accelerator. 
     Speech Recognition Engine 
     Speech recognition is a widely applicable field of ANNs. Speech recognition is to sequentially map analogue signals of a language to a specific set of words. In recent years, methods applying ANNs have achieved much better effects than conventional methods in speech recognition domain, and have become the mainstream in the industry. In particular, DNNs have been widely applied in speech recognition domain. 
     As a practical example of using DNNs, a general frame of the speech recognition engine is shown in  FIG. 4 . 
     In the model shown in  FIG. 4 , it involves computing acoustic output probability using a deep learning model. That is, conducting similarity prediction between a series of input speech signals and various possible candidates. Running the DNN in  FIG. 4  can be accelerated via FPGA, for example. 
       FIG. 5  shows a deep learning model applied in the speech recognition engine of  FIG. 4 . 
     More specifically,  FIG. 5( a )  shows a deep learning model including CNN (Convolutional Neural Network) module, LSTM (Long Short-Term Memory) module, DNN (Deep Neural Network) module, Softmax module, etc. 
       FIG. 5( b )  is a deep learning model where the present invention can be applied, which uses multi-layer LSTM. 
     In the network model shown in  FIG. 5( b ) , the input of the network is a section of voice. For example, for a voice of about 1 second, it will be cut into about 100 frames in sequence, and the characteristics of each frame is represented by a float type vector. 
     LSTM 
     Further, in order to solve long-term information storage problem, Hochreiter &amp; Schmidhuber has proposed the Long Short-Term Memory (LSTM) model in 1997. 
       FIG. 6  shows a LSTM network model applied in speech recognition. LSTM neural network is one type of RNN, which changes simple repetitive neural network modules in normal RNN into complex interconnecting relations. LSTM neural networks have achieved very good effect in speech recognition. 
     For more details of LSTM, prior art references can be made mainly to the following two published papers: Sak H, Senior A W, Beaufays F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[C]//INTERSPEECH. 2014: 338-342; Sak H, Senior A, Beaufays F. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition[J]. arXiv preprint arXiv:1402.1128, 2014. 
     As mentioned above, LSTM is one type of RNN. The main difference between RNNs and DNNs lies in that RNNs are time-dependent. More specifically, for RNNs, the input at time T depends on the output at time T-1. That is, calculation of the current frame depends on the calculated result of the previous frame. 
     In the LSTM architecture of  FIG. 6 : 
     Symbol i represents the input gate i which controls the flow of input activations into the memory cell; 
     Symbol o represents the output gate o which controls the output flow of cell activations into the rest of the network; 
     Symbol f represents the forget gate which scales the internal state of the cell before adding it as input to the cell, therefore adaptively forgetting or resetting the cell&#39;s memory; 
     Symbol g represents the characteristic input of the cell; 
     The bold lines represent the output of the previous frame; 
     Each gate has a weight matrix, and the computation amount for the input of time T and the output of time T-1 at the gates is relatively intensive; 
     The dashed lines represent peephole connections, and the operations correspond to the peephole connections and the three cross-product signs are element-wise operations, which require relatively little computation amount. 
       FIG. 7  shows an improved LSTM network model. 
     As shown in  FIG. 7 , in order to reduce the computation amount of the LSTM layer, an additional projection layer is introduced to reduce the dimension of the model. 
     The LSTM network accepts an input sequence x=(x1, . . . , xT), and computes an output sequence y=(y1, . . . , yT) by using the following equations iteratively from t=1 to T:
 
 i   t =σ( W   ix   x   t   +W   ix   y   t−1   +W   ic   c   t−1   +b   i )
 
 f   t =σ( W   fx   x   t   +W   fr   y   t−1   +W   fc   c   t−1   +b   f )
 
 c   t   =f   t     c   t−1   +i   t     g ( W   cx   x   t   +W   cr   y   t−1   +b   c )
 
 o   t =σ( W   ox   x   t   +W   or   y   t−1   +W   tm   c   i   +b   v )
 
 m   t   =o   t     h ( c   t )
 
y t =W ym m t  
 
     Here, the W terms denote weight matrices (e.g., Wix is the matrix of weights from the input gate to the input), and Wic, Wfc, Woc are diagonal weight matrices for peephole connections which correspond to the three dashed lines in  FIG. 7 . The b terms denote bias vectors (b i  is the gate bias vector), σ is the logistic sigmoid function. The symbols i, f, o, c are respectively the input gate, forget gate, output gate and cell activation vectors, and all of which are the same size as the cell output activation vectors m. ⊙ is the element-wise product of the vectors, g and h are the cell input and cell output activation functions, generally tanh. 
     Since the structure of the above LSTM neural network is rather complicated, it is difficult to achieve desired compression ratio by one single compression. Therefore, the inventors propose an improved multi-iteration compression method for deep neural networks (e.g. LSTM, used in speech recognition), so as to reduce storage resources, accelerate computational speed, reduce power consumption and maintain network accuracy. 
     SUMMARY 
     According to one aspect of the invention, a method for compressing a neural network is proposed, wherein the weights between the neurons of said neural network are characterized by a plurality of matrices. The method comprises: sensitivity analysis step, for analyzing the sensitivity of each of said plurality of matrices, and determining an initial compression ratio for each of said plurality of matrices; compression step, for compressing each of said plurality of matrices based on said initial compression ratio, so as to obtain a compressed neural network; and fine-tuning step, for fine-tuning said compressed neural network. 
     According to another aspect of the invention, a device for compressing a neural network is proposed, wherein the weights between the neurons of said neural network are characterized by a plurality of matrices. The device comprises: a sensitivity analysis unit, which is used for analyzing the sensitivity of each of said plurality of matrices, and determining an initial compression ratio for each of said plurality of matrices; a compression unit, which is used for compressing each of said plurality of matrices based on said initial compression ratio, so as to obtain a compressed neural network; and a fine-tuning unit, which is used for fine-tuning said compressed neural network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a model of deep neural networks; 
         FIG. 2  shows a compression method for neural networks; 
         FIG. 3  shows synapses and neurons before and after pruning; 
         FIG. 4  shows an example of a speech recognition engine using DNNs; 
         FIG. 5  shows a deep learning model applied in the speech recognition engine; 
         FIG. 6  shows a LSTM network model applied in speech recognition; 
         FIG. 7  shows an improved LSTM network model; 
         FIG. 8  shows a multi-iteration compression method for LSTM neural networks according to one embodiment of the present invention; 
         FIG. 9  shows the steps in sensitivity analysis according to one embodiment of the present invention; 
         FIG. 10  shows the corresponding curves obtained by the sensitivity tests according to one embodiment of the present invention; 
         FIG. 11  shows the steps in density determination and pruning according to one embodiment of the present invention; 
         FIG. 12  shows the sub-steps in “Compression-Density Adjustment” iteration according to one embodiment of the present invention; 
         FIG. 13  shows the steps in fine-tuning according to one embodiment of the present invention. 
     
    
    
     EMBODIMENTS OF THE INVENTION 
     Previous Research Products of the Inventor 
     In the article “Learning both weights and connections for efficient neural networks”, Han et al. proposed to prune less influential connections in neural networks (for example, CNNs). The pruning method includes the following steps: 
     Initializing: Initializing the ANN to establish all connections of CONV layers and FC layers, said connections being assigned weights of random values. 
     Training: Training said ANN by adjusting connection weights of CONV layers and FC layers of ANN until the accuracy of ANN reaches a predetermined level. Said training uses a stochastic gradient descent algorithm to adjust weights of ANN. For example, the values of weights are stochastically adjusted, and then some adjusted weights are chosen based on the gradient descent of ANN&#39;s accuracy. The accuracy of ANN can be measured by, for example, inputting a benchmark test data to the ANN and decide how accurate the prediction results of said ANN is. 
     Pruning: Pruning said ANN to remove insignificant connections, said insignificant connections are decided based on a predetermined criteria. More specifically, the weights of the pruned connections are no longer stored. For example, pruning uses at least one of the followings as said predetermined criteria: if the weight of a connection is zero, said connection is insignificant; or, if the weight of a connection is smaller than a threshold, said connection is insignificant. 
     Fine-tuning: Fine-tuning said ANN to restore the pruned connections and assigning zero-value weights to these restored connections. 
     Iteration: Repeating the above-mentioned steps, until the accuracy of ANN reaches a predetermined level. 
     Further Improvements of the Present Invention 
     The present invention further proposes a multi-iteration compression method for deep neural networks. 
       FIG. 8  shows a multi-iteration compression method for LSTM neural networks according to one embodiment of the present invention. It should be noted that the proposed compression method can also be applied in other types of neural networks. 
     According to the embodiment shown in  FIG. 8 , each iteration comprises three steps: sensitivity analysis, pruning and fine-tuning. Now, each step will be explained in detail. 
     Step  8100 : Sensitivity Analysis 
     In this step, sensitivity analysis is conducted for all the matrices in a LSTM network, so as to determine the initial densities (or, the initial compression ratios) for different matrices in the neural network. 
       FIG. 9  shows the specific steps in sensitivity analysis according to the embodiment. 
     As can be seen from  FIG. 9 , in step  8110 , it compresses each matrix in LSTM network according to different densities (for example, the selected densities are 0.1, 0.2 . . . 0.9, and the related compression method is explained in detail in step  8200 ). 
     Next, in step  8120 , it measures the word error ratio (WER) of the neural network compressed under different densities. More specifically, when recognizing a sequence of words, there might be words that are mistakenly inserted, deleted or substituted. For example, for a text of N words, if I words were inserted, D words were deleted and S words were substituted, then the corresponding WER will be:
 
 WER =( I+D+S )/ N.  
 
     WER is usually measured in percentage. In general, the WER of the network after compression will increase, which means that the accuracy of the network after compression will decrease. 
     In step  8120 , for each matrix, it draws a Density-WER curve based on the measured WERs as a function of different densities, wherein x-axis represents the density and y-axis represents the WER of the network after compression. 
     In step  8130 , for each matrix, we locate the point in the Density-WER curve where WER changes most abruptly, and choose the density that corresponds to said point as the initial density. 
     In particular, in the present embodiment, we select the density which corresponds to the inflection point in the Density-WER curve as the initial density of the matrix. More specifically, in one iteration, the inflection point is determined as follows: 
     The WER of the initial neural network before compression in the present iteration is known as WER initial ; 
     The WER of the network after compression according to different densities is: WER 0.1 , WER 0.2  . . . WER 0.9 , respectively; 
     Calculate ΔWER, i.e. comparing WER 0.1  with WER initial , WER 0.2  with WER initial  . . . WER 0.9  with WER initial ; 
     Based on the plurality of calculated ΔWERs, the inflection point refers to the point with the smallest density among all the points with a ΔWER below a certain threshold. However, it should be understood that the point where WER changes most abruptly can be selected according to other criteria, and all such variants shall fall into the scope of the present invention. 
     In one example, for a LSTM network with 3 layers where each layer comprises 9 dense matrices (Wix, Wfx, Wcx, Wox, Wir, Wfr, Wcr, Wor, and Wrm) to be compressed, a total number of 27 dense matrices need to be compressed. 
     First of all, for each matrix, conducting 9 compression tests with different densities ranging from 0.1 to 0.9 with a step of 0.1. Then, for each matrix, measuring the WER of the whole network after each compression test, and drawing the corresponding Density-WER curve. Therefore, for a total number of 27 matrices, we obtain 27 curves. 
     Next, for each matrix, locating the inflection point in the corresponding Density-WER curve. Here, we assume that the inflection point is the point with the smallest density among all the points with a ΔWER below 1%. 
     For example, in the present iteration, assuming the WER of the initial neural network before compression is 24%, then the point with the smallest density among all the points with a WER below 25% is chosen as the inflection point, and the corresponding density of this inflection point is chosen as the initial density of the corresponding matrix. 
     In this way, we can obtain an initial density sequence of 27 values, each corresponding to the initial density of the corresponding matrix. Thus, this sequence can be used as guidance for further compression. 
     An example of the initial density sequence is as follows, wherein the order of the matrices is Wcx, Wix, Wfx, Wox, Wcr, Wir, Wfr, Wor, and Wrm.
         densityList=[0.2, 0.1, 0.1, 0.1, 0.3, 0.3, 0.1, 0.1, 0.3, 0.5, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.3, 0.4, 0.3, 0.1, 0.2, 0.3, 0.3, 0.1, 0.2, 0.5]       

       FIG. 10  shows the corresponding Density-WER curves of the 9 matrices in one layer of the LSTM neural network. As can be seen from  FIG. 10 , the sensitivity of each matrix to be compressed differs dramatically. For example, w_g_x, w_r_m, w_g_r are more sensitive to compression as there are points with max (ΔWER)&gt;1% in their Density-WER curves. 
     Step  8200 : Density Determination and Pruning 
       FIG. 11  shows the specific steps in density determination and pruning. 
     As can be seen from  FIG. 11 , step  8200  comprises several sub-steps. 
     First of all, in step  8210 , compressing each matrix based on the initial density sequence determined in step  8130 . 
     Then, in step  8215 , measuring the WER of the neural network obtained in step  8210 . If ΔWER of the network before and after compression is above certain threshold ε, for example, 4%, then go to the next step  8220 . 
     In step  8220 , adjusting the initial density sequence via “Compression-Density Adjustment” iteration. 
     In step  8225 , obtaining the final density sequence. 
     In step  8215 , if ΔWER of the neural network before and after compression does not exceed said threshold ε, then it goes to step  8225  directly, and the initial density sequence is set as the final density sequence. 
     Lastly, in step  8230 , pruning the LSTM neural network based on the final density sequence. 
     Now, each sub-step in  FIG. 11  will be explained in more detail. 
     Step  8210 , conducting an initial compression test on the basis of an initial density. 
     Based on previous studies, the weights with larger absolute values in a matrix correspond to stronger connections between the neurons. Thus, in this embodiment, compression is made according to the absolute values of elements in a matrix. However, it should be understood that other compression strategy can be used, and all of which fall into the scope of the present invention. 
     According to one embodiment of the present invention, in each matrix, all the elements are ranked from small to large according to their absolute values. Then, each matrix is compressed according to the initial density determined in Step  8100 , and only a corresponding ratio of elements with larger absolute values are remained, while other elements with smaller values are set to zero. For example, if the initial density of a matrix is 0.4, then only 40% of the elements in said matrix with larger absolute values are remained, while the other 60% of the elements with smaller absolute values are set to zero. 
     Step  8215 , determining whether ΔWER of the network before and after compression is above certain threshold ε, for example, 4%. 
     Step  8220 , if ΔWER of the network before and after compression is above said threshold ε, for example, 4%, then conducting the “Compression-Density Adjustment” iteration. 
     Step  8225 , obtaining the final density sequence through density adjustment performed in step  8220 . 
       FIG. 12  shows the specific steps in the “Compression-Density Adjustment” iteration. 
     As can be seen in  FIG. 12 , in step  8221 , it adjusts the density of the matrices that are relatively sensitive. That is, for each sensitive matrix, increasing its initial density, for example, by 0.05. Then, conducting a compression test for said matrix based on the adjusted density. 
     In the embodiment of the present invention, the compression test is conducted in a similar manner as the initial compression test of step  8210 . However, it should be understood that other compression strategies can be used, all of which fall into the scope of the present invention. 
     Then, it calculates the WER of the network after compression. If the WER is still unsatisfactory, continuing to increase the density of corresponding matrix, for example, by 0.1. Then, conducting a further compression test for said matrix based on the re-adjusted density. Repeating the above steps until ΔWER of the network before and after compression is below said threshold ε, for example, 4%. 
     Optionally or sequentially, in step  8222 , the density of the matrices that are less sensitive can be adjusted slightly, so that ΔWER of the network before and after compression can be below certain threshold ε′, for example, 3.5%. In this way, the accuracy of the network after compression can be further improved. 
     As can be seen in  FIG. 12 , the process for adjusting insensitive matrices is similar to that for sensitive matrices. 
     In one example, the initial WER of a network is 24.2%, and the initial density sequence of the network obtained in step  8100  is:
         densityList=[0.2, 0.1, 0.1, 0.1, 0.3, 0.3, 0.1, 0.1, 0.3, 0.5, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.3, 0.4, 0.3, 0.1, 0.2, 0.3, 0.3, 0.1, 0.2, 0.5],       

     After pruning the network according to the initial density sequence, the WER of the compressed network is worsened to be 32%, which means that the initial density sequence needs to be adjusted. The steps for adjusting the initial density sequence is as follows: 
     According to the result in step 8100, as shown in  FIG. 10 , Wcx, Wcr, Wir, Wrm in the first layer, Wcx, Wcr, Wrm in the second layer, and Wcx, Wix, Wox, Wcr, Wir, Wor, Wrm in the third layer are relatively sensitive, while the other matrices are insensitive. 
     First of all, increasing the initial densities of the above sensitive matrices by 0.05, respectively. 
     Then, conducting compression tests based on the increased density. The resulting WER after compression is 27.7%, which meets the requirement of ΔWER&lt;4%. Thus, the step for adjusting the densities of sensitive matrices is completed. 
     According to another embodiment of the present invention, optionally, the density of matrices that are less sensitive can be adjusted slightly, so that ΔWER of the network before and after compression will be below 3.5%. In this example, this step is omitted. 
     Thus, the final density sequence obtained via “Compression-Density Adjustment” iteration is as follows:
         densityList=[ 0.25, 0.1, 0.1, 0.1, 0.35, 0.35, 0.1, 0.1, 0.35, 0.55, 0.1, 0.1, 0.1, 0.25, 0.1, 0.1, 0.1, 0.35, 0.45, 0.35, 0.1, 0.25, 0.35, 0.35, 0.1, 0.25, 0.55]       

     The overall density of the neural network after compression is now around 0.24. 
     Step  8230 , pruning based on the final density sequence. 
     For example, in the present embodiment, pruning is also based on the absolute values of the elements in the matrices. 
     More specifically, for each matrix, all elements are ranked from small to large according to their absolute values. Then, each matrix is compressed according to its final density, and only a corresponding ratio of elements with larger absolute values are remained, while other elements with smaller values are set to zero. 
     Step  8300 , Fine Tuning 
     Neural network training is a process for optimizing loss function. Loss function refers to the difference between the ideal result and the actual result of a neural network model under predetermined input. It is therefore desirable to minimize the value of loss function. 
     Indeed, the essence of neural network training lies in the search of optimal solution. Fine tuning (or, retraining) is to search the optimal solution based on a suboptimal solution. That is, continuing to train the neural network on certain basis. 
       FIG. 13  shows the specific steps in fine-tuning. 
     As can be seen from  FIG. 13 , the input of fine-tuning is the neural network after pruning in step  8200 . 
     In step  8310 , it trains the sparse neural network obtained in step  8200  with a predetermined data set, and updates the weight matrix. 
     Then, in step  8320 , it determines whether the matrix has converged to a local sweet point. If not, it goes back to step  8310  and repeats the training and updating process; and if yes, it goes to step  8330  and obtains the final neural network. 
     In one specific embodiment of the present invention, stochastic gradient descent algorithm is used during fine-tuning to update the weight matrix. 
     More specifically, if real-value function F(x) is differentiable and has definition at point a, then F(x) descents the fastest along —∇F(a) at point a. 
     Thus, if:
 
 b=a−γ∇F ( a )
 
is true when γ&gt;0 is a value that is small enough, then F(a)≥F(b), wherein a is a vector.
 
     In light of this, we can start from x 0  which is the local minimal value of function F, and consider the following sequence x 0 , x 1 , x 2 , . . . , so that:
 
 x   n+1   =x   n −γ n   ∇F ( x   n ),  n≥ 0
 
     Thus, we can obtain:
 
 F ( x   0 )≥ F ( x   1 )≥ F ( x   2 )≥
 
     Desirably, the sequence (x n ) will converge to the desired extreme value. It should be noted that in each iteration, step γ can be changed. 
     Here, F(x) can be interpreted as a loss function. In this way, stochastic gradient descent algorithm can help reducing prediction loss. 
     In one example, and with reference to “DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow in NIPS 2016”, the fine-tuning method of LSTM neural network is as follows: 
     
       
         
           
               
             
               
                   
               
               
                 Initial Dense Phase 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                   
                 while not converged do 
               
               
                   
                  | Ŵ (t)  = W (t−1)  − η (t)  ∇ f (W (t−1) ; x (t−1) ); 
               
               
                   
                  | t = t + 1; 
               
               
                   
                 end 
               
               
                   
                   
               
            
           
         
       
     
     Here, W refers to weight matrix, η refers to learning rate (that is, the step of the stochastic gradient descent algorithm), f refers to loss function, ∇F refers to gradient of the loss function, x refers to training data, and t+1 refers to weight update. 
     The above equations means updating the weight matrix by subtracting the product of learning rate and gradient of the loss function (η*∇F) from the weight matrix. 
     In another example, a mask matrix containing only “0” and “1” is used to keep the distribution of non-zero elements in the matrix after compression. 
     In general, fine-tuning with mask is as follows:
 
 W   (t)   =W   (t−1) −η (j)   ∇f ( W   (t−1)   ; x   (t−1) )·Mask
 
Mask=( W   (0) ≠0)
 
     As can be seen from the above equations, the gradient of the loss function is multiplied by the mask matrix, assuring that the gradient matrix will have the same shape as the mask matrix. 
     The following is a specific example illustrating an exemplary fine-tuning process and convergence criteria. 
     In this example, the input of fine-tuning includes: the network to be trained, the learning rate, max_iters (which refers to the maximum number of training iterations), min_iters (which refers to the minimum number of training iterations), keep_lr_iters (which refers to the number of iterations that keep the initial learning rate), start_halving_impr (which is used for determining when to change the learning rate, for example, 0.01), end_halving_impr (which is used to determine when to terminate the training, for example, 0.001), halving_factor (for example, 0.5), data sets (including training set, cross-validation set, testing set), etc. 
     In addition, the input of fine-tuning also includes parameters such as learning momentum, num-stream, batch-size, etc., all of which are omitted detailed description herein. 
     The output of fine-tuning is the network after training. 
     The detail process of fine-tuning is as follows: 
     1. Testing the cross loss (hereinafter referred to as “loss”) of the initial network model to be trained using the cross-validation set, wherein the loss is the initial standard to evaluate the performance of network training; 
     2. Iterative Training:
         Iterative training is divided into several “epochs”, wherein an epoch (hereinafter referred to as “one iteration”) means that all data in the training dataset has been run for once, and the total number of iterations shall not be more than max_iters or less than min_iters;   In each iteration, updating the weight matrix of the network using the stochastic gradient descent algorithm and the training dataset;   After each iteration, storing the trained network and testing its loss using the cross-validation set. If the loss of the present iteration is larger than that of the previous valid training (referred to as loss_prev), than the present iteration is rejected and the next iteration will still be conducted based on the result of the previous iteration; and if the loss of the present iteration is smaller than loss_prev, the present iteration is accepted and the next iteration will be conducted based on the result of the present iteration, and the loss of the present iteration is stored;   Conditions for learning rate modification and training termination: input parameters related to learning rate modification and training termination includes: start_halving_impr, end_halving_impr, halving_factor, etc. After each iteration, calculating the improvement (referred to as real impr) based on (loss_prev-loss)/loss_prev, wherein real impr refers to the relative improvement of the loss of the present iteration compared to that of the previous iteration. Then, based on real_impr, we have:       

     1) If the number of iterations is less than keep_lr_iters, then keep the learning rate unchanged; 
     2) If real_impr is less that start_halving_impr (for example, 0.01), that is, if the improvement of the present iteration compared to that of the previous iteration is within a relatively small range, which means that the network is close to its local sweet point, then it decreases the learning rate by multiplying said learning rate by halving_factor (which is usually 0.5). In this way, the step of the stochastic gradient descent algorithm is decreased, so that the network will approach the local sweet point with smaller step; 
     3) If real_impr is less that end_halving_impr (for example, 0.001), that is, if the improvement of the present iteration compared to that of the previous iteration is very small, then it terminates the training. However, if the number of iterations is smaller than min_iters, then it continues the training until the number of iterations reaches min_iters. 
     Thus, there will be four possible results when the training terminates, which are: 
     1. If the network is trained for min_iters and during which real_impr is always larger than end_halving_impr, then it takes the result of the final iteration; 
     2. If the network is trained for min_iters and during which real_impr being smaller than end_halving_impr occurs, then it takes the result of the iteration with the minimal loss; 
     3. If the network is trained for more than min_iters but less than max_iters and during which real_impr being smaller than end_halving_impr occurs, then it takes the result of the final iteration, i.e. the result of the iteration with the minimal loss; 
     4) If the network is trained for max_iters and during which real_impr is always larger than end_halving_impr, then take the result of the final iteration. 
     It should be noted that the above example shows one possible fine-tuning process and a convergence criteria to determine whether the matrix has converged to its local sweet point. However, in practical operations, in order to improve compression efficiency, it is not necessary to wait for the final convergence result. It could take an intermediate result and start the next iteration. 
     Moreover, convergence criteria can also be whether the WER of the trained network meets certain standard, for example. It should be understood that these criteria also fall into the scope of the present invention. 
     Thus, the WER of the network decreases via fine-tuning, reducing accuracy loss due to compression. For example, the WER of a compressed LSTM network with a density of 0.24 can drop from 27.7% to 25.8% after fine-tuning. 
     Iteration (Repeating  8100 ,  8200  and  8300 ) 
     Referring again to  FIG. 8 , as mentioned above, the neural network will be compressed to a desired density via multi-iteration, that is, by repeating the above-mentioned steps  8100 ,  8200  and  8300 . 
     For example, the desired final density of one exemplary neural network is 0.14. 
     After the first iteration, the network obtained after Step  8300  has a density of 0.24 and a WER of 25.8%. 
     Then, steps  8100 ,  8200  and  8300  are repeated. 
     After the second iteration, the network obtained after Step  8300  has a density of 0.18 and a WER of 24.7%. 
     After the third iteration, the network obtained after Step  8300  has a density of 0.14 and a WER of 24.6% which meets the requirements. 
     Beneficial Technical Effects 
     Based on the above technical solution, the present invention proposes a multi-iteration compression method for deep neural networks. The method has the following beneficial effects: 
     1. Saving Storage Space 
     By adopting this method, dense neural network can be compressed into sparse neural network with the same accuracy but substantially less non-zero parameters. It reduces the storage space, makes on-chip storage possible, and improves computational efficiency. 
     For example, for a LSTM network, the density of the network after compression can be below 0.15, and the relative accuracy loss of the network can be less than 5%. 
     2. Accelerating Compression Process 
     As mentioned above, fine-tuning is time consuming. According to a preferred embodiment of the present invention, in order to compress the neural network to the target density with shorter time, by means of the proposed sensitive analysis, the inflection point in the 
     Density-WER curve is used in determining the initial density of corresponding matrix. In this way, the compression process can be accelerated. 
     Moreover, in another preferred embodiment of the present invention, during fine-tuning, the WER criteria can be adopted in determining whether the network has converged to its local sweet point. In this way, the number of iterations during fine-tuning can be reduced, and the whole process can be accelerated. 
     It should be understood that although the above-mentioned embodiments use LSTM neural networks as examples of the present invention, the present invention is not limited to LSTM neural networks, but can be applied to various other neural networks as well. 
     Moreover, those skilled in the art may understand and implement other variations to the disclosed embodiments from a study of the drawings, the present application, and the appended claims. 
     In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. 
     In applications according to present application, one element may perform functions of several technical feature recited in claims. 
     Any reference signs in the claims should not be construed as limiting the scope. The scope and spirit of the present application is defined by the appended claims.