Abstract:
An error correction circuit is provided which uses NMOS and PMOS synapses to form neural network type responses to a coded multi-bit input. Use of MOS technology logic in error correction circuits allows such devices to be easily interfaced with other like technology circuits without the need to use distinct interface logic as with conventional error correction circuitry.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to an error correction circuit and more particularly to an error correction circuit which is based on a neural network model. 
     2. Background of the Invention 
     A data processing system made of conventional logic circuits is getting bigger in size and more complex in its arrangement of components. As a result, increasing circuit complexity creates unexpected problems and rising manufacturing costs. 
     In addition, the need to improve accuracy and reliability of every block in the system or its respective subsystems, demands that techniques for providing error correction be included. However, systems based on simple logic circuits have performance limitations due to inherent property characteristics of logic gates. 
     To overcome such limitations of logic circuit technologies a system design based on the concept of a neural network model has been actively studied. 
     An error correcting system based on neural network principles is shown in FIG. 1. This system was presented in the IEEE first annual international conference on neural networks in June 1987, which has a reference number of IEEE catalog #87TH0191-7, by Yoshiyasu Takefuji, Paul Hollis, Yoon Pin Foo, and Yong B. Cho. 
     The error correcting system presented at the above conference uses the concept of neural network principles, based on the Hopfield model, and discloses a circuit which performs significantly faster than prior error correcting systems. 
     However, since the circuit by Yoshiyasu Takefuji et al. uses operational amplifiers as neurons and a passive resistor element network to form synapses, VLSI implementation is quite limited. The reason being that on a semiconductor integrated circuit, a resistor element network has high power consumption and thus hinders manufacturing of a high integration circuit design. 
     Furthermore, the above circuit is further inadequate since it requires additional interfacing circuitry added whenever a digital system based on NMOS and CMOS technologies is coupled thereto. 
     SUMMARY OF THE INVENTION 
     Accordingly, it is an object to provide an error correction circuit having a design which is based on neural network principles and which uses MOS transistors formed on semiconductor VLSI logic. 
     It is another object to provide an error correction circuit which is directly connectable to conventional NMOS and CMOS digital system technology without requiring additional interface logic. 
     In achieving the above objects, the present invention is characterized in that the circuit comprises: 
     n neurons; 
     2 k  inverters; 
     a plurality of first synapses; 
     a plurality of second synapses; 
     a plurality of biasing synapses; 
     a plurality of third synapses; and 
     n transmitting gates. 
     The neurons are buffer amplifiers in which two CMOS inverters are connected in cascade. A synapse for transferring an excitatory state includes a PMOS transistor, and a synapse for transferring an inhibitory state includes a NMOS transistor. 
     The connecting strength of synapses is determined by the geometrical aspect ratio W/L of each respective transistor and corresponds to the ratio of a transistor&#39;s channel width to its channel length. 
     In the present invention, the error correction circuit is made using CMOS technology and thus designed to be directly interfaceable to other CMOS and related technologies without the use of additional interface logic. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The above objects and other advantages of the present invention will become more apparent by describing the preferred embodiment of the present invention with reference to the attached drawings, in which: 
     FIG. 1 is a circuit diagram of a preferred embodiment showing a conventional error correction circuit having a design based on neural network principles; and 
     FIG. 2 is a circuit diagram of a preferred embodiment showing an error correction circuit according to the present invention. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     In error correcting circuits, an (n,k) code word represents k actual data bits and n-k check bits. That is, the codeword is n bits long and contains k actual data bits. Generally, an (n,k) code can generate all 2 k  codes by using the following polynomial generating equation. The equation is 
     
         C(X)=D(X)*G(X) 
    
     where, C(X) is a codeword polynomial of the degree lower than 
     
         n-1, 
    
     D(X) is a data polynomial of the degree lower than k-1, and 
     G(X) is a generating polynomial of the (n-k) th degree. 
     Thus, encoding the data polynomial D(X) means getting the codeword polynomial C(X) from D(X) multiplied by G(X). 
     In a 1 bit error correction circuit of (7,4) codewords, when the generating polynomial of G(X)=X 3  +X+1 is given to code a 4 bit data string as a (7,4) codeword, the following (7,4) codewords shown in Table 1 are obtained. 
     
                                           TABLE 1__________________________________________________________________________Data bits             C(X) = D(X) · G(X)X.sub.3  X.sub.2    X.sub.1 X.sub.0   D(X)    G(X)  X.sub.6                    X.sub.5                      X.sub.4                         X.sub.3                           X.sub.2                              X.sub.1                                X.sub.0__________________________________________________________________________0 0 0 0               0  0 0  0 0  0 00 0 0 1 1             0  0 0  1 0  1 10 0 1 0 X             0  0 1  0 1  1 00 0 1 1 X+1           0  0 1  1 1  0 10 1 0 0 X.sup.2       0  1 0  0 1  1 10 1 0 1 X.sup.2 +1    0  1 0  1 1  0 00 1 1 0 X.sup.2 +X           X.sup.3 +X+1                 0  1 1  0 0  0 10 1 1 1 X.sup.2 +X+1  0  1 1  1 0  1 01 0 0 0 X.sup.3       1  0 0  0 1  0 11 0 0 1 X.sup.3 +1    1  0 0  1 1  1 01 0 1 0 X.sup.3 +X    1  0 1  0 0  1 11 0 1 1 X.sup.3 +X+1  1  0 1  1 0  0 01 1 0 0 X.sup.3 +X.sup.2                 1  1 0  0 0  1 01 1 0 1 X.sup.3 +X.sup.2 +1                 1  1 0  1 0  0 11 1 1 0 X.sup.3 +X.sup.2 +X                 1  1 1  0 1  0 01 1 1 1 X.sup.3 +X.sup.2 +X+1                 1  1 1  1 1  1 1__________________________________________________________________________ 
    
     As shown in Table 1, when only 1 bit errors can occur, the number of possible errors for each coded 4 bit data string equals 7. For example, code pattern &#34;1011000&#34; is explained in detail in Table 2. 
     
                       TABLE 2______________________________________The error states of code pattern&#34;1011000&#34;______________________________________0011000111100010010001010000101110010110101011001______________________________________ 
    
     As shown in Table 2, each 1 bit error state of &#34;1011000&#34; does not match any of the other codewords. In connection with the smallest Hamming distance, the number of check bits is calculated by using the following equation: 
     
         Df≧2 t+1 
    
     where, t is the number of corrected bits, and 
     Df is the number of check bits. 
     In FIG. 2, a 1 bit error correction circuit is shown as a (7,4) codeword according to the present invention. 
     Into the right bottom part on FIG. 2, an input is entered, and then outputted to the left bottom. The right portion is the vector unit portion 10, and the left portion is the storage unit portion 20. 
     Between these two portions, feedback via inverters INV1 to INV16 is carried out and the signal amplified. 
     Transmitting gates TG1 to TG7 are used in the input portion so as to control the input signal in response to clock signal CK. This is to prevent the feedback signal and the input signal from becoming superposed. 
     The signal passed through transmitting gates TG1 to TG7 then freely passes to vector unit portion 10 and then fed through first and second synapses 21 and 22 of storage unit portion 20. 
     Storage unit portion 20 comprises 7 neurons N1 to N7, first and second synapses 21 and 22, and biasing synapses 23. 
     The respective neurons N1 to N7, each of which consist of two interconnected CMOS inverters, have as their output lines noninverted output lines NRL1 to NRL7 and inverted output lines RL1 to RL7. 
     Vector unit portion 10 comprises 16 inverters INV1 to INV16 and third synapse 11. The NMOS transistors (i.e., first synapses 21) are selectively disposed at respective intersections of positions corresponding to a &#34;0&#34; value for all 16 codewords shown in Table 1 at the intersections of the noninverted output lines and the input lines of the inverters. 
     Second synapses 21 (i.e., PMOS transistors) are selectively disposed at respective intersections of positions corresponding to a &#34;1&#34; value. 
     If the noninverted output lines connected to the gates of NMOS transistors are at a &#34;HIGH&#34; state, the respective NMOS transistors are turned on and the inhibitory state is set (i.e., Vss or ground potential), in unit connecting strength to the input line to which the drain is connected. 
     If the inverted output lines connected to the gates of the PMOS transistors are in a &#34;LOW&#34; state, the PMOS transistors are turned on and the excitatory state is set (i.e., Vcc or supplying voltage), in unit connecting strength to the input line to which the drain is connected. 
     The unit connecting strength (i.e., the value of W/L) of a PMOS transistor is 6/2 (μm/μm) and of an NMOS transistor is 2/2 (μm/μm). 
     When the excitatory strength is equal to the inhibitory strength, the unit connecting strength of the excitatory state (i.e., the conductance of the PMOS transistor) is superior to the conductance of the NMOS transistor and so the state is recognized as an excitatory state. 
     Furthermore, biasing synapses 23 (i.e., NMOS and PMOS transistors) are connected to respective input lines of the inverters. Biasing synapses 23 have a connecting strength value determined by subtracting the number of bits to be corrected from the number of second synapses 22 connected to the respective input lines of the particular biasing synapse. 
     In the case of code pattern &#34;0001011&#34; with 1 bit error correction, the biasing synapse connected to the input line of the second inverter INV2 has 3 PMOS transistors. Hence, the NMOS biasing transistor is included to transfer an inhibitory state with a connecting strength of 3-1=2. 
     This NMOS transistor has a geometrical aspect ratio of W/L=2.(2/2) (μm/μm). 
     The biasing synapse connected to the output line of first inverter INV1 has value &#34;0&#34; since the number of PMOS transistors is zero and error correction is for 1 bit error. Therefore, a transistor is included to transfer the excitatory state with a connecting strength of 0-1=1. 
     Biasing synapses 23 provide only the output line of the inverter which codeword is the most similar pattern to the synapse pattern connected to the input line among the 16 codewords and sets that to an excitatory state. 
     Hence, the output line will have value &#34;0&#34; and the other 15 output lines, inverted and set to inhibitory states will yield &#34;1&#34; states. 
     More specifically, biasing synapses 23 cause the input lines of INV1-16 to be high or lo in accordance with the following rules: 
     1. If (A-B)+C&gt;D, 
     THEN TRANSFER INHIBITORY STATE 
     2. IF (A-B)+C←D, 
     THEN TRANSFER EXCITATORY STATE 
     where 
     A is the number of PMOS (second) synapses in the word which should be transferring an excitatory state, 
     B is the number of PMOS (second) synapses in the word which actually are transferring an excitatory state, 
     C is the number of NMOS (first) synapses in the word which actually are transferring an inhibitory state, and 
     D is the number of bits the code corrects. 
     The implementation of these rules is accomplished by connecting the biasing synapses with a connecting strength equal to: 
     (#of PMOS (second) synapses in a word)-(#of bits the code corrects). 
     In vector unit portion 10, the self feedback pattern is detected and the signal outputted as the final output. 
     In third synapse 11 of vector unit 10, to transfer the inhibitory state as the unit connecting strength at each intersection corresponding to the respective &#34;0&#34; bit values of the above codeword among intersections of the input lines of the neurons of N1 to N7 and the output lines of the inverters of INV1 to INV7, to the interconnecting input line, and the excitatory state having 2 4-1  =8 as the connecting strength at each intersection corresponding to the respective &#34;1&#34; bit values, NMOS and PMOS transistors are used. 
     Here, the geometrical aspect ratio of each NMOS transistor is W/L=2/2 (μm/μm) and the geometrical aspect ratio of each PMOS transistor is W/L=48/2 (μm/μm). The reason is that only one has &#34;0&#34; value while the other 15 all have value &#34;1&#34; as they pass through a respective inverter. 
     In this particular example, a PMOS transistor and 8 NMOS transistors can be turned on. After this state, to yield a value &#34;1&#34;, the geometrical aspect ratio of the PMOS transistor should be W/L=8(6/2) (μm/μm). That is, when the connecting strength of the excitation is equal to that of the inhibition, the excitatory state is eminently activated. 
     The following Table 3 shows the output results as a function of input data for the error correction circuit of FIG. 2. 
     
                       TABLE 3______________________________________INPUT DATAOUTPUT DATA______________________________________0000000    0001011    0010110    00111010000001    0001010    0010111    00111000000010    0001001    0010110    00111110000100    0001111    0010010    00110010001000    0000011    0011110    00101010010000    0011011    0000110    00011010100000    0101011    0110110    01111011000000    1001011    1010110    10111010000000    0001011    0010110    00111010100111    0101100    0110001    01110100100110    0101101    0110000    01110110100101    0101010    0110011    01110000100011    0101000    0110101    01111100101111    0100100    0111001    01100100110111    0111100    0100001    01010100000111    0001100    0010001    00110101100111    1101100    1110001    11110100100111    0101100    0110001    01110101000101    1001110    1010011    10110001000100    1001111    1010010    10110011000111    1001100    1010001    10110101000001    1001010    1010111    10111001001101    1000110    1011011    10100001010101    1011110    1000011    10010001100101    1101110    1110011    11110000000101    0001110    0010011    00110000000000    1001110    1010011    10110001100010    1101001    1110100    11111111100011    1101000    1110101    11111101100000    1101011    1110110    11111011100110    1101101    1110000    11110111101010    1100001    1111100    11101111110010    1111001    1100100    11011111000010    1001001    1010100    10111110100010    0101001    0110100    01111111100010    1101001    1110100    1111111______________________________________ 
    
     Therefore, the present invention uses NMOS and PMOS transistors in a neural network based model and includes CMOS buffer amplifiers as neurons, to easily achieve VLSI implementation and easy interfacing to like technology circuits.