Patent Publication Number: US-10311149-B1

Title: Natural language translation device

Description:
FIELD 
     This patent document generally relates to the field of machine learning. More particularly the present document relates to natural language translation device using a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based Integrated Circuit embedded therein. 
     BACKGROUND 
     An ideogram is a graphic symbol that represents an idea or concept. Some ideograms are comprehensible only by familiarity with prior convention; others convey their meaning through pictorial resemblance to a physical object. Each ideogram can represent a word in a natural language including, but not limited to, a Chinese character, a Japanese character, a Korean character, an English word, etc. 
     Machine learning is an application of artificial intelligence. In machine learning, a computer or computing device is programmed to think like human beings so that the computer may be taught to learn on its own. The development of neural networks has been key to teaching computers to think and understand the world in the way human beings do. Many deep learning networks such as Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have been developed in the past decade. 
     Natural language translations using a computer system have been around for a while. Prior art approaches predominantly rely on recurrent neural networks (RNN) to capture the semantic meaning of a piece of text before converting it from one natural language to another. RNN are complex and thereby difficult to implement in a semi-conductor chip. Accordingly, it would be desired to have improved methods and systems for translating natural language using a CNN based integrated circuit without RNN. 
     SUMMARY 
     This section is for the purpose of summarizing some aspects of the invention and to briefly introduce some preferred embodiments. Simplifications or omissions in this section as well as in the abstract and the title herein may be made to avoid obscuring the purpose of the section. Such simplifications or omissions are not intended to limit the scope of the invention. 
     The natural language translation apparatus are disclosed. According to one aspect of the disclosure, a natural language translation device contains a bus, an input interface connecting to the bus for receiving a source sentence in a first natural language to be translated to a target sentence in second natural language one word at a time in sequential order. A two-dimensional (2-D) symbol containing super-character characterizing the i-th word of the target sentence based on the received source sentence is formed in accordance with a set of 2-D symbol creation rules. The i-th word of the target sentence is obtained by classifying the 2-D symbol via a deep learning model that contains multiple ordered convolution layers in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit. 
     According to another aspect of the disclosure, a digital integrated circuit contains cellular neural networks (CNN) processing engines operatively coupled to at least one input/output data bus. The CNN processing engines are connected in a loop with a clock-skew circuit. Each CNN processing engine includes a CNN processing block configured for simultaneously performing convolutional operations using input imagery data (2-D symbol) and pre-trained filter coefficients of the plurality of ordered convolutional layers. The first set of memory buffers operatively couples to the CNN processing block for storing the input imagery data. The second set of memory buffers operative couples to the CNN processing block for storing the pre-trained filter coefficients. 
     According to another aspect, a first example set of 2-D symbol creation rules contains the following actions: dividing the 2-D symbol into first and second parts; converting the source sentence to a first set of ideograms; converting the first word to the (i−1)-th word of the target sentence to a second set of ideograms; and forming the 2-D symbol by including the first set of ideograms in the first part and including the second set of ideograms in the second part. 
     According to yet another aspect, a second example set of 2-D symbol creation rules contains the following actions: converting the source sentence to a set of ideograms; dividing the 2-D symbol into a predetermined number of sub-symbols with each sub-symbol for one ideogram; and forming the 2-D symbol by including relevant portion of the set of ideograms in a scheme such that the ideogram corresponding to the i-th word is located in a predefined location, by filling each unoccupied sub-symbol with blank space if needed. 
     According to still another aspect, a third example set of 2-D symbol creation rules contains the following actions: dividing the 2-D symbol into a predetermined number of sub-symbols; creating the predetermined number of groups of consecutive words from the source sentence with each group encompassing the i-th word of the source sentence and said each group corresponding to one of the sub-symbols; and forming the 2-D symbol by including ideograms converted from respective groups of consecutive words for all sub-symbols. 
     Objects, features, and advantages of the invention will become apparent upon examining the following detailed description of an embodiment thereof, taken in conjunction with the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the invention will be better understood with regard to the following description, appended claims, and accompanying drawings as follows: 
         FIG. 1  is a diagram illustrating an example two-dimensional symbol comprising a matrix of N×N pixels of data that represents a super-character for facilitating machine learning of a combined meaning of multiple ideograms contained therein according to an embodiment of the invention; 
         FIGS. 2A-2B  are diagrams showing example partition schemes for dividing the two-dimensional symbol of  FIG. 1  in accordance with embodiments of the invention; 
         FIGS. 3A-3B  show example ideograms in accordance with an embodiment of the invention; 
         FIG. 3C  shows example pictograms containing western languages based on Latin letters in accordance with an embodiment of the invention; 
         FIG. 3D  shows three respective basic color layers of an example ideogram in accordance with an embodiment of the invention; 
         FIG. 3E  shows three related layers of an example ideogram for dictionary-like definition in accordance with an embodiment of the invention; 
         FIG. 4A  is a block diagram illustrating an example Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system for machine learning of a combined meaning of multiple ideograms contained in a two-dimensional symbol, according to one embodiment of the invention; 
         FIG. 4B  is a block diagram illustrating an example CNN based integrated circuit for performing image processing based on convolutional neural networks, according to one embodiment of the invention; 
         FIG. 5  is a flowchart illustrating an example process of natural language translation using a CNN based integrated circuit in accordance with an embodiment of the invention; 
         FIG. 6  is a schematic diagram showing dataflow of an example natural language translation device in accordance with an embodiment of the invention; 
         FIG. 7  is a schematic diagram showing an example image processing technique based on convolutional neural networks in accordance with an embodiment of the invention; 
         FIG. 8  is a diagram showing an example CNN processing engine in a CNN based integrated circuit, according to one embodiment of the invention; 
         FIG. 9  is a diagram showing an example imagery data region within the example CNN processing engine of  FIG. 8 , according to an embodiment of the invention; 
         FIGS. 10A-10C  are diagrams showing three example pixel locations within the example imagery data region of  FIG. 9 , according to an embodiment of the invention; 
         FIG. 11  is a diagram illustrating an example data arrangement for performing 3×3 convolutions at a pixel location in the example CNN processing engine of  FIG. 8 , according to one embodiment of the invention; 
         FIGS. 12A-12B  are diagrams showing two example 2×2 pooling operations according to an embodiment of the invention; 
         FIG. 13  is a diagram illustrating a 2×2 pooling operation of an imagery data in the example CNN processing engine of  FIG. 8 , according to one embodiment of the invention; 
         FIGS. 14A-14C  are diagrams illustrating various examples of imagery data region within an input image, according to one embodiment of the invention; 
         FIG. 15  is a diagram showing a plurality of CNN processing engines connected as a loop via an example clock-skew circuit in accordance of an embodiment of the invention; 
         FIG. 16  is a schematic diagram showing a first example two-dimensional symbol used for natural language translation in accordance with an embodiment of the invention; 
         FIG. 17  is a schematic diagram showing a second example two-dimensional symbol used for natural language translation in accordance with an embodiment of the invention; 
         FIG. 18  is a schematic diagram showing a third example two-dimensional symbol used for natural language translation in accordance with an embodiment of the invention; 
         FIG. 19  is a flowchart illustrating an example process of achieving a trained convolutional neural networks model having bi-valued 3×3 filter kernels in accordance with an embodiment of the invention; 
         FIG. 20A  is a diagram showing an example data conversion scheme; 
         FIG. 20B  is a diagram showing an example filter kernel conversion scheme in accordance with the invention; 
         FIG. 21  is a function diagram showing a first example natural language translation device in accordance with one embodiment of the invention; and 
         FIG. 22  is a function diagram showing a second example natural language translation device in accordance with one embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTIONS 
     In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will become obvious to those skilled in the art that the invention may be practiced without these specific details. The descriptions and representations herein are the common means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art. In other instances, well-known methods, procedures, and components have not been described in detail to avoid unnecessarily obscuring aspects of the invention. 
     Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Used herein, the terms “vertical”, “horizontal”, “diagonal”, “left”, “right”, “upper”, “lower”, “top”, “bottom”, “column”, “row”, “diagonally” are intended to provide relative positions for the purposes of description, and are not intended to designate an absolute frame of reference. Additionally, used herein, term “character” and “script” are used interchangeably. 
     Embodiments of the invention are discussed herein with reference to  FIGS. 1-22 . However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these figures is for explanatory purposes as the invention extends beyond these limited embodiments. 
     Referring first to  FIG. 1 , it is shown a diagram showing an example two-dimensional symbol  100  for facilitating machine learning of a combined meaning of multiple ideograms contained therein. The two-dimensional symbol  100  comprises a matrix of N×N pixels (i.e., N columns by N rows) of data containing a super-character. Pixels are ordered with row first and column second as follows: (1,1), (1,2), (1,3), . . . (1,N), (2,1), . . . , (N,1), . . . (N,N). N is a positive integer or whole number, for example in one embodiment, N is equal to 224. 
     super-character represents at least one meaning each formed with a specific combination of a plurality of ideograms. Since an ideogram can be represented in a certain size matrix of pixels, two-dimensional symbol  100  is divided into M×M sub-matrices. Each of the sub-matrices represents one ideogram, which is defined in an ideogram collection set by humans. super-character contains a minimum of two and a maximum of M×M ideograms. Both N and M are positive integers or whole numbers, and N is preferably a multiple of M. 
     Shown in  FIG. 2A , it is a first example partition scheme  210  of dividing a two-dimension symbol into M×M sub-matrices  212 . M is equal to 4 in the first example partition scheme. Each of the M×M sub-matrices  212  contains (N/M)×(N/M) pixels. When N is equal to 224, each sub-matrix contains 56×56 pixels and there are 16 sub-matrices. 
     A second example partition scheme  220  of dividing a two-dimension symbol into M×M sub-matrices  222  is shown in  FIG. 2B . M is equal to 8 in the second example partition scheme. Each of the M×M sub-matrices  222  contains (N/M)×(N/M) pixels. When N is equal to 224, each sub-matrix contains 28×28 pixels and there are 64 sub-matrices. 
       FIG. 3A  shows example ideograms  301 - 304  that can be represented in a sub-matrix  222  (i.e., 28×28 pixels). For those having ordinary skill in the art would understand that the sub-matrix  212  having 56×56 pixels can also be adapted for representing these ideograms. The first example ideogram  301  is a pictogram representing an icon of a person riding a bicycle. The second example ideogram  302  is a logosyllabic script or character representing an example Chinese character. The third example ideogram  303  is a logosyllabic script or character representing an example Japanese character and the fourth example ideogram  304  is a logosyllabic script or character representing an example Korean character. Additionally, ideogram can also be punctuation marks, numerals or special characters. In another embodiment, pictogram may contain an icon of other images. Icon used herein in this document is defined by humans as a sign or representation that stands for its object by virtue of a resemblance or analogy to it. 
       FIG. 3B  shows several example ideograms representing: a punctuation mark  311 , a numeral  312  and a special character  313 . Furthermore, pictogram may contain one or more words of western languages based on Latin letters, for example, English, Spanish, French, German, etc.  FIG. 3C  shows example pictograms containing western languages based on Latin letters. The first example pictogram  326  shows an English word “MALL”. The second example pictogram  327  shows a Latin letter “Ü” and the third example pictogram  328  shows English alphabet “Y”. Ideogram can be any one of them, as long as the ideogram is defined in the ideogram collection set by humans. 
     Only limited number of features of an ideogram can be represented using one single two-dimensional symbol. For example, features of an ideogram can be black and white when data of each pixel contains one-bit. Feature such as grayscale shades can be shown with data in each pixel containing more than one-bit. 
     Additional features are represented using two or more layers of an ideogram. In one embodiment, three respective basic color layers of an ideogram (i.e., red, green and blue) are used collectively for representing different colors in the ideogram. Data in each pixel of the two-dimensional symbol contains a K-bit binary number. K is a positive integer or whole number. In one embodiment, K is 5. 
       FIG. 3D  shows three respective basic color layers of an example ideogram. Ideogram of a Chinese character are shown with red  331 , green  332  and blue  333 . With different combined intensity of the three basic colors, a number of color shades can be represented. Multiple color shades may exist within an ideogram. 
     In another embodiment, three related ideograms are used for representing other features such as a dictionary-like definition of a Chinese character shown in  FIG. 3E . There are three layers for the example ideogram in  FIG. 3E : the first layer  341  showing a Chinese logosyllabic character, the second layer  342  showing the Chinese “pinyin” pronunciation as “wang”, and the third layer  343  showing the meaning in English as “king”. 
     Ideogram collection set includes, but is not limited to, pictograms, icons, logos, logosyllabic characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters, etc. 
     In order to systematically include Chinese characters, a standard Chinese character set (e.g., GB18030) may be used as a start for the ideogram collection set. For including Japanese and Korean characters, CJK Unified Ideographs may be used. Other character sets for logosyllabic characters or scripts may also be used. 
     A specific combined meaning of ideograms contained in a super-character is a result of using image processing techniques in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system. Image processing techniques include, but are not limited to, convolutional neural networks, recurrent neural networks, etc. 
     super-character represents a combined meaning of at least two ideograms out of a maximum of M×M ideograms. In one embodiment, a pictogram and a Chinese character are combined to form a specific meaning. In another embodiment, two or more Chinese characters are combined to form a meaning. In yet another embodiment, one Chinese character and a Korean character are combined to form a meaning. There is no restriction as to which two or more ideograms to be combined. 
     Ideograms contained in a two-dimensional symbol for forming super-character can be arbitrarily located. No specific order within the two-dimensional symbol is required. Ideograms can be arranged left to right, right to left, top to bottom, bottom to top, or diagonally. 
     Using written Chinese language as an example, combining two or more Chinese characters may result in a super-character including, but not limited to, phrases, idioms, proverbs, poems, sentences, paragraphs, written passages, articles (i.e., written works). In certain instances, the super-character may be in a particular area of the written Chinese language. The particular area may include, but is not limited to, certain folk stories, historic periods, specific background, etc. 
     Referring now to  FIG. 4A , it is shown a block diagram illustrating an example CNN based computing system  400  configured for machine learning of a combined meaning of multiple ideograms contained in a two-dimensional symbol (e.g., the two-dimensional symbol  100 ). 
     The CNN based computing system  400  may be implemented on integrated circuits as a digital semi-conductor chip (e.g., a silicon substrate) and contains a controller  410 , and a plurality of CNN processing units  402   a - 402   b  operatively coupled to at least one input/output (I/O) data bus  420 . Controller  410  is configured to control various operations of the CNN processing units  402   a - 402   b , which are connected in a loop with a clock-skew circuit. 
     In one embodiment, each of the CNN processing units  402   a - 402   b  is configured for processing imagery data, for example, two-dimensional symbol  100  of  FIG. 1 . 
     To store an ideogram collection set, one or more storage units operatively coupled to the CNN based computing system  400  are required. Storage units (not shown) can be located either inside or outside the CNN based computing system  400  based on well known techniques. 
     Super-character may contain more than one meanings in certain instances. super-character can tolerate certain errors that can be corrected with error-correction techniques. In other words, the pixels represent ideograms do not have to be exact. The errors may have different causes, for example, data corruptions, during data retrieval, etc. 
     In another embodiment, the CNN based computing system is a digital integrated circuit that can be extendable and scalable. For example, multiple copies of the digital integrated circuit may be implemented on a single semi-conductor chip as shown in  FIG. 4B . 
     All of the CNN processing engines are identical. For illustration simplicity, only few (i.e., CNN processing engines  422   a - 422   h ,  432   a - 432   h ) are shown in  FIG. 4B . There is no limit as to the number of CNN processing engines on a digital semi-conductor chip. 
     Each CNN processing engine  422   a - 422   h ,  432   a - 432   h  contains a CNN processing block  424 , a first set of memory buffers  426  and a second set of memory buffers  428 . The first set of memory buffers  426  is configured for receiving imagery data and for supplying the already received imagery data to the CNN processing block  424 . The second set of memory buffers  428  is configured for storing filter coefficients and for supplying the already received filter coefficients to the CNN processing block  424 . In general, the number of CNN processing engines on a chip is 2 n , where n is an integer (i.e., 0, 1, 2, 3, . . . ). As shown in  FIG. 4B , CNN processing engines  422   a - 422   h  are operatively coupled to a first input/output data bus  430   a  while CNN processing engines  432   a - 432   h  are operatively coupled to a second input/output data bus  430   b . Each input/output data bus  430   a - 430   b  is configured for independently transmitting data (i.e., imagery data and filter coefficients). In one embodiment, the first and the second sets of memory buffers comprise random access memory (RAM), which can be a combination of one or more types, for example, Magnetic Random Access Memory, Static Random Access Memory, etc. Each of the first and the second sets are logically defined. In other words, respective sizes of the first and the second sets can be reconfigured to accommodate respective amounts of imagery data and filter coefficients. 
     The first and the second I/O data bus  430   a - 430   b  are shown here to connect the CNN processing engines  422   a - 422   h ,  432   a - 432   h  in a sequential scheme. In another embodiment, the at least one I/O data bus may have different connection scheme to the CNN processing engines to accomplish the same purpose of parallel data input and output for improving performance. 
       FIG. 5  is a flowchart illustrating an example process  500  of natural languages translation using a CNN based integrated circuit  400 . Process  500  can be implemented in software as an application module installed in at least one computer system. Process  500  may also be implemented in hardware (e.g., integrated circuits). 
     Process  500  starts at action  502  by receiving a source sentence or phrase in a first natural language to be translated to a target sentence phrase in a second natural language one word at a time in sequential order. The source sentence is received via an input interface of a natural language translation device (e.g., example natural language translation devices  2100 - 2200 ). Next, at action  504 , an index “i” is set or initialized to the beginning of the target sentence. The index “i” is used for indicating which word of the target sentence is currently being translated. Terms ‘sentence’ and ‘phrase’ are used interchangeably in this disclosure. 
     Then, at action  506 , a multi-layer two-dimensional (2-D) symbol is formed according to a set of 2-D symbol creation rules using a 2-D symbol creation module installed on the natural language translation device. The 2-D symbol contains a super-character having a characteristic suggesting the i-th word of the target sentence based on the received source sentence. Example 2-D symbols are shown in  FIGS. 16-18  and corresponding descriptions for the 2-D symbol creation rules. 
     At action  508 , the i-th word of the target sentence is obtained by classifying the 2-D symbol via a deep learning model that contains multiple ordered convolutional layers in a CNN based integrated circuit. 
     Next, at decision  510 , it is determined whether the obtained i-th word is an end-of-sentence (EOS) marker. The end of a target sentence includes such a marker to indicate the termination of a sentence. If not, process  500  following the ‘no’ branch to action  512 . The index “i” is incremented to the next word of the target sentence. Then process  500  repeats actions  506 - 508  until decision  510  becomes true. Process  500  ends thereafter. 
       FIG. 6  is a schematic diagram showing dataflow of an example natural language translation device (e.g., devices  2100 - 2200  shown in  FIGS. 21-22 ). A source sentence in first natural language  610  is received via an input interface (not shown) operatively coupled to processing unit  620 . The source sentence  610  is to be translated to a target sentence in a second natural language one word at a time in sequential order. 
     Using a 2-D symbol creation module  622  installed thereon, processing unit  620  forms a 2-D symbol  631   a - 631   c  (i.e., an image contained in a matrix of N×N pixels of data in multiple layers) containing M×M ideograms  632  (e.g., two-dimensional symbol  100  of  FIG. 1 ) in accordance with a set of 2-D symbol creation rules derived from the received source sentence  610 . Each two-dimensional symbol  631   a - 631   c  is a matrix of N×N pixels of data containing a super-character. The matrix is divided into M×M sub-matrices representing respective M×M ideograms. Super-character represents a characteristic suggesting the i-th word of the target sentence. M and N are positive integers or whole numbers, and N is preferably a multiple of M. More details of forming the multi-layer two-dimensional symbol are shown in  FIG. 7  and corresponding descriptions. 
     The i-th word is obtained by classifying the multi-layer two-dimensional symbol  631   a - 631   c  via a deep learning model (e.g., image processing technique  638  using convolutional neural networks) in CNN based integrated circuits implemented on a semi-conductor chip shown in  FIG. 4A . 
     The image processing technique  638  includes predefining a set of categories  642  (e.g., “Category-1”, “Category-2”, . . . “Category-X” shown in  FIG. 6 ). As a result of performing the image processing technique  638 , respective probabilities  644  of the categories are determined for associating each of the predefined categories  642  with the meaning/characteristic of the super-character. In the example shown in  FIG. 6 , the highest probability of 88.08 percent is shown for “Category-2”. In other words, the multi-layer two-dimensional symbol  631   a - 631   c  contains a super-character whose characteristic (i.e., the i-th word of the target sentence) has a probability of 88.08 percent associated with “Category-2” amongst all the predefined categories  644 . 
     In one embodiment, the multi-layer two-dimensional symbol  631   a - 631   c  contains three layers for red, green and blue hues. Each pixel in each layer of the two-dimension symbol contains K-bit. In one embodiment, K=8 for supporting true color, which contains 256 shades of red, green and blue. In another embodiment, K=5 for a reduced color map having 32 shades of red, green and blue. 
       FIG. 7  is a schematic diagram showing an example image processing technique based on convolutional neural networks. 
     Based on convolutional neural networks, a multi-layer two-dimensional symbol  711   a - 711   c  as input imagery data is processed with convolutions using a first set of filters or weights  720 . Since the imagery data of the 2-D symbol  711   a - 711   c  is larger than the filters  720 . Each corresponding overlapped sub-region  715  of the imagery data is processed. After the convolutional results are obtained, activation may be conducted before a first pooling operation  730 . In one embodiment, activation is achieved with rectification performed in a rectified linear unit (ReLU). As a result of the first pooling operation  730 , the imagery data is reduced to a reduced set of imagery data  731   a - 731   c . For 2×2 pooling, the reduced set of imagery data is reduced by a factor of 4 from the previous set. 
     The previous convolution-to-pooling procedure is repeated. The reduced set of imagery data  731   a - 731   c  is then processed with convolutions using a second set of filters  740 . Similarly, each overlapped sub-region  735  is processed. Another activation can be conducted before a second pooling operation  740 . The convolution-to-pooling procedures are repeated for several layers and finally connected to a Fully-Connected Layers  760 . In image classification, respective probabilities  644  of predefined categories  642  can be computed in Fully-Connected Layers  760 . 
     This repeated convolution-to-pooling procedure is trained using a known dataset or database. For image classification, the dataset contains the predefined categories. A particular set of filters, activation and pooling can be tuned and obtained before use for classifying an imagery data, for example, a specific combination of filter types, number of filters, order of filters, pooling types, and/or when to perform activation. In one embodiment, the imagery data is the multi-layer two-dimensional symbol  711   a - 711   c , which is form from a string of natural language texts. 
     In one embodiment, convolutional neural networks are based on a Visual Geometry Group (VGG16) architecture neural nets. 
     More details of a CNN processing engine  802  in a CNN based integrated circuit are shown in  FIG. 8 . A CNN processing block  804  contains digital circuitry that simultaneously obtains Z×Z convolution operations results by performing 3×3 convolutions at Z×Z pixel locations using imagery data of a (Z+2)-pixel by (Z+2)-pixel region and corresponding filter coefficients from the respective memory buffers. The (Z+2)-pixel by (Z+2)-pixel region is formed with the Z×Z pixel locations as an Z-pixel by Z-pixel central portion plus a one-pixel border surrounding the central portion. Z is a positive integer. In one embodiment, Z equals to 14 and therefore, (Z+2) equals to 16, Z×Z equals to 14×14=196, and Z/2 equals 7. 
       FIG. 9  is a diagram showing a diagram representing (Z+2)-pixel by (Z+2)-pixel region  910  with a central portion of Z×Z pixel locations  920  used in the CNN processing engine  802 . 
     In order to achieve faster computations, few computational performance improvement techniques have been used and implemented in the CNN processing block  804 . In one embodiment, representation of imagery data uses as few bits as practical (e.g., 5-bit representation). In another embodiment, each filter coefficient is represented as an integer with a radix point. Similarly, the integer representing the filter coefficient uses as few bits as practical (e.g., 12-bit representation). As a result, 3×3 convolutions can then be performed using fixed-point arithmetic for faster computations. 
     Each 3×3 convolution produces one convolution operations result, Out(m, n), based on the following formula: 
                     Out   ⁡     (     m   ,   n     )       =         ∑       1   ≤   i     ,     j   ≤   3         ⁢       In   ⁡     (     m   ,   n   ,   i   ,   j     )       ×     C   ⁡     (     i   ,   j     )           -   b             (   1   )               
where:
         m, n are corresponding row and column numbers for identifying which imagery data (pixel) within the (Z+2)-pixel by (Z+2)-pixel region the convolution is performed;   In(m,n,i,j) is a 3-pixel by 3-pixel area centered at pixel location (m, n) within the region;   C(i, j) represents one of the nine weight coefficients C(3×3), each corresponds to one of the 3-pixel by 3-pixel area;   b represents an offset or bias coefficient; and   i, j are indices of weight coefficients C(i, j).       

     Each CNN processing block  804  produces Z×Z convolution operations results simultaneously and, all CNN processing engines perform simultaneous operations. In one embodiment, the 3×3 weight or filter coefficients are each 12-bit while the offset or bias coefficient is 16-bit or 18-bit. 
       FIGS. 10A-10C  show three different examples of the Z×Z pixel locations. The first pixel location  1031  shown in  FIG. 10A  is in the center of a 3-pixel by 3-pixel area within the (Z+2)-pixel by (Z+2)-pixel region at the upper left corner. The second pixel location  1032  shown in  FIG. 10B  is one pixel data shift to the right of the first pixel location  1031 . The third pixel location  1033  shown in  FIG. 10C  is a typical example pixel location. Z×Z pixel locations contain multiple overlapping 3-pixel by 3-pixel areas within the (Z+2)-pixel by (Z+2)-pixel region. 
     To perform 3×3 convolutions at each sampling location, an example data arrangement is shown in  FIG. 11 . Imagery data (i.e., In(3×3)) and filter coefficients (i.e., weight coefficients C(3×3) and an offset coefficient b) are fed into an example CNN 3×3 circuitry  1100 . After 3×3 convolutions operation in accordance with Formula (1), one output result (i.e., Out(1×1)) is produced. At each sampling location, the imagery data In(3×3) is centered at pixel coordinates (m, n)  1105  with eight immediate neighbor pixels  1101 - 1104 ,  1106 - 1109 . 
     Imagery data are stored in a first set of memory buffers  806 , while filter coefficients are stored in a second set of memory buffers  808 . Both imagery data and filter coefficients are fed to the CNN block  804  at each clock of the digital integrated circuit. Filter coefficients (i.e., C(3×3) and b) are fed into the CNN processing block  804  directly from the second set of memory buffers  808 . However, imagery data are fed into the CNN processing block  804  via a multiplexer MUX  805  from the first set of memory buffers  806 . Multiplexer  805  selects imagery data from the first set of memory buffers based on a clock signal (e.g., pulse  812 ). 
     Otherwise, multiplexer MUX  805  selects imagery data from a first neighbor CNN processing engine (from the left side of  FIG. 8  not shown) through a clock-skew circuit  820 . 
     At the same time, a copy of the imagery data fed into the CNN processing block  804  is sent to a second neighbor CNN processing engine (to the right side of  FIG. 8  not shown) via the clock-skew circuit  820 . Clock-skew circuit  820  can be achieved with known techniques (e.g., a D flip-flop  822 ). 
     After 3×3 convolutions for each group of imagery data are performed for predefined number of filter coefficients, convolution operations results Out(m, n) are sent to the first set of memory buffers via another multiplex MUX  807  based on another clock signal (e.g., pulse  811 ). An example clock cycle  810  is drawn for demonstrating the time relationship between pulse  811  and pulse  812 . As shown pulse  811  is one clock before pulse  812 , as a result, the 3×3 convolution operations results are stored into the first set of memory buffers after a particular block of imagery data has been processed by all CNN processing engines through the clock-skew circuit  820 . 
     After the convolution operations result Out(m, n) is obtained from Formula (1), activation procedure may be performed. Any convolution operations result, Out(m, n), less than zero (i.e., negative value) is set to zero. In other words, only positive value of output results are kept. For example, positive output value 10.5 retains as 10.5 while −2.3 becomes 0. Activation causes non-linearity in the CNN based integrated circuits. 
     If a 2×2 pooling operation is required, the Z×Z output results are reduced to (Z/2)×(Z/2). In order to store the (Z/2)×(Z/2) output results in corresponding locations in the first set of memory buffers, additional bookkeeping techniques are required to track proper memory addresses such that four (Z/2)×(Z/2) output results can be processed in one CNN processing engine. 
     To demonstrate a 2×2 pooling operation,  FIG. 12A  is a diagram graphically showing first example output results of a 2-pixel by 2-pixel block being reduced to a single value 10.5, which is the largest value of the four output results. The technique shown in  FIG. 12A  is referred to as “max pooling”. When the average value 4.6 of the four output results is used for the single value shown in  FIG. 12B , it is referred to as “average pooling”. There are other pooling operations, for example, “mixed max average pooling” which is a combination of “max pooling” and “average pooling”. The main goal of the pooling operation is to reduce the size of the imagery data being processed.  FIG. 13  is a diagram illustrating Z×Z pixel locations, through a 2×2 pooling operation, being reduced to (Z/2)×(Z/2) locations, which is one fourth of the original size. 
     An input image generally contains a large amount of imagery data. In order to perform image processing operations, an example input image  1400  (e.g., a two-dimensional symbol  100  of  FIG. 1 ) is partitioned into Z-pixel by Z-pixel blocks  1411 - 1412  as shown in  FIG. 14A . Imagery data associated with each of these Z-pixel by Z-pixel blocks is then fed into respective CNN processing engines. At each of the Z×Z pixel locations in a particular Z-pixel by Z-pixel block, 3×3 convolutions are simultaneously performed in the corresponding CNN processing block. 
     Although there is no requirement for specific characteristic dimension of an input image, the input image may be required to resize to fit into a predefined characteristic dimension for certain image processing procedures. In an embodiment, a square shape with (2 L ×Z)-pixel by (2 L ×Z)-pixel is required. L is a positive integer (e.g., 1, 2, 3, 4, etc.). When Z equals 14 and L equals 4, the characteristic dimension is 224. In another embodiment, the input image is a rectangular shape with dimensions of (2 I ×Z)-pixel and (2 J ×Z)-pixel, where I and J are positive integers. 
     In order to properly perform 3×3 convolutions at pixel locations around the border of a Z-pixel by Z-pixel block, additional imagery data from neighboring blocks are required.  FIG. 14B  shows a typical Z-pixel by Z-pixel block  1420  (bordered with dotted lines) within a (Z+2)-pixel by (Z+2)-pixel region  1430 . The (Z+2)-pixel by (Z+2)-pixel region is formed by a central portion of Z-pixel by Z-pixel from the current block, and four edges (i.e., top, right, bottom and left) and four corners (i.e., top-left, top-right, bottom-right and bottom-left) from corresponding neighboring blocks. 
       FIG. 14C  shows two example Z-pixel by Z-pixel blocks  1422 - 1424  and respective associated (Z+2)-pixel by (Z+2)-pixel regions  1432 - 1434 . These two example blocks  1422 - 1424  are located along the perimeter of the input image. The first example Z-pixel by Z-pixel block  1422  is located at top-left corner, therefore, the first example block  1422  has neighbors for two edges and one corner. Value “0”s are used for the two edges and three corners without neighbors (shown as shaded area) in the associated (Z+2)-pixel by (Z+2)-pixel region  1432  for forming imagery data. Similarly, the associated (Z+2)-pixel by (Z+2)-pixel region  1434  of the second example block  1424  requires “0”s be used for the top edge and two top corners. Other blocks along the perimeter of the input image are treated similarly. In other words, for the purpose to perform 3×3 convolutions at each pixel of the input image, a layer of zeros (“0”s) is added outside of the perimeter of the input image. This can be achieved with many well-known techniques. For example, default values of the first set of memory buffers are set to zero. If no imagery data is filled in from the neighboring blocks, those edges and corners would contain zeros. 
     When more than one CNN processing engine is configured on the integrated circuit. The CNN processing engine is connected to first and second neighbor CNN processing engines via a clock-skew circuit. For illustration simplicity, only CNN processing block and memory buffers for imagery data are shown. An example clock-skew circuit  1540  for a group of example CNN processing engines are shown in  FIG. 15 . 
     CNN processing engines connected via the second example clock-skew circuit  1540  to form a loop. In other words, each CNN processing engine sends its own imagery data to a first neighbor and, at the same time, receives a second neighbor&#39;s imagery data. Clock-skew circuit  1540  can be achieved with well-known manners. For example, each CNN processing engine is connected with a D flip-flop  1542 . 
     In order to use 2-D symbols in a natural language translation device (e.g., devices  2100 ,  2200 ) that translates a source sentence in first natural language to a target sentence in second natural language one word at a time in sequential order, the super-character contained in the 2-D symbol is designed to possess a characteristic suggesting the i-th word of the target sentence. Three such example 2-D symbols are shown in  FIGS. 16-18 . 
     The first example 2-D symbol  1620  shown in  FIG. 16  is divided into the upper part  1621  and the lower part  1622 . There are other schemes to divide the first 2-D symbol  1620 , for example, left half and right half. The upper part  1621  includes ideograms created from the words in the entire source sentence  1610 . Each ideogram corresponds to a word in the source sentence  1610 . In this example, the source sentence contains words (S 1 , S 2 , S 3 , . . . , S n-2 , S n-1 , S n ). Any remaining space in the upper part  1621  is filled with blank or default. The lower part  1622  includes ideograms created from the already-translated portion  1641  of the target sentence  1640 . Any remaining space in the lower part  1622  is filled with blank or default. Already-translated portion  1641  corresponds to partial target sentence (T 1 , T 2 , . . . , T i-2 , T i-1 ) which are the characteristics suggesting the i-th word (T i )  1630  of the target sentence  1640 . Each ideogram in the lower part  1622  corresponds to one of the words in the already-translated portion  1641  of the target sentence  1640 . 
       FIG. 17  shows the second example 2-D symbol  1720 . This symbol  1720  contains the i-th word (S i )  1711  of the source sentence  1710 , including a predetermined number of words before and after the i-th word (S i )  1711 . The second example 2-D symbol  1720  demonstrates eight words preceding and seven succeeding the i-th word (S i )  1711 , but such specific numbers are predetermined based on different natural languages used in the translation. Blank buffer spaces are extended to either end of the source sentence  1710  as necessary. For example, when the first word (i.e., i=1), eight preceding blank buffer spaces are used for creating the 2-D symbol. 
     In order to provide the characteristic suggesting the i-th word (T i )  1730  of the target sentence  1740 . The ideogram representing the i-th word (S i )  1711  of the source sentence  1710  is placed in a predefined location  1722  in the 2-D symbol  1720 . 
     The third example 2-D symbol  1820  is shown in  FIG. 18 . The third example 2-D symbol  1820  is divided into a predetermined number of sub-symbols. In this example, there are four sub-symbols  1821 - 1824 . Other predetermined number may be used for achieving the same, for example, nine or any other positive integers. Then, four corresponding groups of consecutive words with each group encompasses the i-th word (S 1 )  1811  of the source sentence  1810 . The third example 2-D symbol  1820  is formed by including ideograms converted from respective groups of consecutive words for all sub-symbols  1821 - 1824 . Similar to first and second example 2-D symbols, the third example 2-D symbol  1820  possesses a characteristic suggesting the i-th word (T i )  1830  of the target sentence  1840 . 
     Training of a deep or machine learning model (e.g., convolutional neural networks) can be achieved with an example set of operations  1900  shown in  FIG. 19 . At action  1902 , a deep learning model is trained by learning the image category or class of a labeled dataset, which contains a sufficiently large number of multi-layer 2-D symbols. For example, there are at least many thousands of 2-D symbols for each category. In other words, each 2-D symbol in the labeled dataset is associated with a category to be classified. The deep learning model includes multiple ordered convultional layers or filter groups. Each filter in the multiple ordered filter groups contains a standard 3×3 filter kernel (i.e., nine coefficients in floating point number format (e.g., standard 3×3 filter kernel  2010  in  FIG. 20B )). Each of the nine coefficients can be any negative or positive real number (i.e., a number with fraction). The initial deep learning model may be obtained from many different frameworks including, but not limited to, Mxnet, caffe, tensorflow, etc. 
     Then, at action  1904 , the deep learning model is modified by converting respective standard 3×3 filter kernels  2010 , shown in  FIG. 20B , to corresponding bi-valued 3×3 filter kernels  2020  of a currently-processed filter group in the multiple ordered filter groups based on a set of kernel conversion schemes. In one embodiment, each of the nine coefficients C(i,j) in the corresponding bi-valued 3×3 filter kernel  2020  is assigned a value ‘A’ equal to the average of absolute coefficient values multiplied by the sign of corresponding coefficients in the standard 3×3 filter kernel  2010  shown in following formula: 
     
       
         
           
             
               
                 
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     Filter groups are converted one at a time in the order defined in the multiple ordered filter groups. In certain situation, two consecutive filter groups are optionally combined such that the training of the convolutional neural networks model is more efficient. 
     Next, at action  1906 , the modified deep learning model is retrained until a desired convergence criterion is met or achieved. There are a number of well known convergence criteria including, but not limited to, completing a predefined number of retraining operation, converging of accuracy loss due to filter kernel conversion, etc. In one embodiment, all filter groups including already converted in previous retraining operations can be changed or altered for fine tuning. In another embodiment, the already converted filter groups are frozen or unaltered during the retraining operation of the currently-processed filter group. 
     Process  1900  moves to decision  1908 , it is determined whether there is another unconverted filter group. If ‘yes’, process  1900  moves back to repeat actions  1904 - 1906  until all filter groups have been converted. Decision  1908  becomes ‘no’ thereafter. At action  1910 , coefficients of bi-valued 3×3 filter kernels in all filter groups are transformed from a floating point number format to a fixed point number format to accommodate the data structure required in the CNN based integrated circuit. Furthermore, the fixed point number is implemented as reconfigurable circuits in the CNN based integrated circuit. In one embodiment, the coefficients are implemented using a fixed point number format with 12-bit mantissa, 2-bit exponent and 1-bit for sign. The fixed point number format can be adapted in accordance with different types of input imagery data. 
       FIG. 20A  is a diagram showing an example data conversion scheme for converting an imagery data (e.g., 2-D symbol) from 8-bit [0-255] to 5-bit [0-31] per pixel. For example, bits  0 - 7  becomes 0, bits  8 - 15  becomes 1, etc. 
     A first example natural language translation device  2100  is shown in  FIG. 21 . The first example device  2100  contains at least an input interface  2116  (e.g., camera, scanner, touch screen, tablet pen, keyboard, etc.), a processing unit  2112  (e.g., computer central processing unit, computer graphics processing unit. etc.), a CNN based integrated circuit  2102  (e.g., CNN based integrated circuit  400  in  FIG. 4A ), a memory  2104  (e.g., Dynamic Random Access Memory or other suitable alternative storage), and a display unit  2118  (e.g., display screen). All of which are operatively connected to a bus  2110 . 
     The first example natural language translation device  2100  is an embedded system using CNN based integrated circuit  2102  for computations of convolutional layers using pre-trained filter coefficients stored therein. Memory  2104  is configured for storing at least the received source sentence. The processing unit  2112  controls input interface  2116  to receive a source sentence in a first natural language to be translated to a target sentence in a second natural language one word at a time in sequential order. Processing unit  2112  then forms a two-dimensional (2-D) symbol in accordance with a set of 2-D symbol creation rules using a 2-D symbol creation module installed thereon. The 2-D symbol contains a super-character having a characteristic suggesting the i-th word of the target sentence based on the received source sentence. Example 2-D symbols are shown in  FIGS. 16-18 . 
     The 2-D symbol is an imagery data that can be classified using a CNN based integrated circuit  2102  via a deep learning model. The deep learning model contained at least multiple ordered convolutional layers, fully-connected layers, pooling operations and activation operations. Display device  2118  displays already-translated portion of the target sentence. Because the translation is performed one word at a time in a sequential order, the entire target sentence is shown at the end of translation. 
     Multiple ordered convolutional layers contain pre-trained filter coefficients in forms of bi-valued 3×3 filter kernels. Each of the bi-valued 3×3 filter kernels contains fixed point number with positive or negative of same numerical value. An example of bi-valued 3×3 filter kernel is shown in  FIG. 20B . The filter coefficients for convolutional layers and weight coefficients for fully-connected layers are trained using a labeled database containing sufficient amount of imagery data categorized with a set of defined classes (i.e., words of the second natural language). Training of deep learning model can be performed using process  1900  in  FIG. 19 . Training can also be performed directly using bi-valued 3×3 filter kernels without any transformation from floating point number format to fixed point number format. 
       FIG. 22  shows a second example natural language translation device  2200 , which contains a dongle  2201  and a host  2200  (e.g., a mobile phone) connected through a bus  2210  (e.g., USB—Universal Serial Bus). 
     Dongle  2201  contains a CNN based integrated circuit  2202  and a DRAM (Dynamic Random Access Memory)  2204 . Host  2220  contains a processing unit  2222 , memory  2224 , input interface  2226  and display screen  2228 . In one embodiment, when the host  2220  is a mobile phone, the input means  2226  can be through the display screen  2228  as touch screen input. 
     Although the invention has been described with reference to specific embodiments thereof, these embodiments are merely illustrative, and not restrictive of, the invention. Various modifications or changes to the specifically disclosed example embodiments will be suggested to persons skilled in the art. For example, whereas the second example 2-D symbol  1720  has been shown and described with the i-th word  1711  at the left most position  1722  in the lower half. Other locations within the 2-D symbol  1720  may be used for achieving the same. Additionally, whereas the third example 2-D symbol  1820  has been shown and described with four groups, the invention does not set a limit to the number of groups, other numbers of groups may be used, for example, nine groups. Furthermore, whereas the example 2-D symbol has been shown and described to contain 16 ideograms, other number of ideograms may be used, for example, 25. Finally, whereas the two-dimensional symbol has been described and shown with a specific example of a matrix of 224×224 pixels, other sizes may be used for achieving substantially similar objections of the invention. In summary, the scope of the invention should not be restricted to the specific example embodiments disclosed herein, and all modifications that are readily suggested to those of ordinary skill in the art should be included within the spirit and purview of this application and scope of the appended claims.