 Open Access
 Total Downloads : 150
 Authors : Niviya. S, Poorani. A, Induja. R, Sargunam. B
 Paper ID : IJERTV3IS041052
 Volume & Issue : Volume 03, Issue 04 (April 2014)
 Published (First Online): 19042014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Error Control Methods for AllOne Polynomial Based Galois Field Multiplier
Niviya. S UG Scholar Department of Electronics and Communication
Engineering, Avinashilingam University for Women Coimbatore, Tamil Nadu, India.
Poorani. A UG Scholar Department of Electronics and Communication Engineering, Avinashilingam
University for Women Coimbatore, Tamil Nadu, India.
Induja. R UG Scholar Department of Electronics and Communication
Engineering, Avinashilingam University for Women Coimbatore, Tamil Nadu, India.
Sargunam. B Assistant professor Department of Electronics and Communication Engineering, Avinashilingam University for Women Coimbatore, Tamil Nadu, India.
Abstract Finite field multiplier has wide applications in elliptic curve cryptography and error control coding systems. Error detection and correction is an effective way to mitigate attacks in cryptographic hardware. Security threats arising from injected soft (transient) errors into a cryptographic circuit can expose the secret information. In this paper, Triple Modular Redundancy (TMR) techniques and parity prediction technique based on Hamming for error detection and correction are used in all one polynomial based finite field multiplier. In triple modular redundancy technique there are three systems which perform same process and the results are processed by a majority voting system to produce a single output. Parity Prediction technique are used to detect and correct single error based on the Hamming principle. TMR techniques and parity prediction technique are compared based on area, delay and power consumption. Parity prediction technique is better than TMR in terms of area and power consumption.
Keywords finite field multipliers, irreducible AOP, error detection and correction, triple modular redundancy, parity prediction.

INTRODUCTION
The Finite field finds its main application in the field of computer algebra, error control coding theory, public key cryptosystem and elliptic curve cryptography [1] [2]. Among all the finite field arithmetic operations like addition, multiplication, exponentiation and multiplicative inversion, the multiplication occupies an important and irreplaceable part since exponentiation and multiplicative inversion can be carried out by iterative multiplication.
One important factor that affects the hardware efficiency in finite fields is the basis chosen to represent a field element. There are a few bases available for the finite field such as polynomial/standard/canonical basis, normal basis and dual basis [3]. Multipliers based on some popular polynomials such as AllOne Polynomials (AOP) and trinomials have low circuit complexity [47]. The irreducible all one polynomial seems to be more efficient for both hardware and software implementations [8].
Fault based cryptanalysis attack is a recently developed cryptanalysis approach where faults are injected into cryptosystems. Inducing faults into the computation of elliptic curve scalar multiplication easily enables recovery of the secret key. There are many error detection approaches developed for private key cryptosystems and public key cryptosystems to check the output values. The error detection methods for polynomial basis multipliers employ parity checking.
A number of error correction schemes have been proposed in the literature [913] for error detection. The Hamming distance between the code words must be greater than one; otherwise it cannot be used to detect an error. J. Mathew and A. M. Jabir proposed a bit parallel GF multiplier [14] for single error detection and correction. The principal techniques that are being used for single error correction are: (1) error detection and retry, (2) error masking, and (3) using encoded operands.
In this paper we have used Triple Modular Redundancy (TMR) and Parity prediction (Hamming) techniques for error detection and error correction. In TMR there are three systems which perform the multiplication process and the result of the multiplication is given to a majority voting system to produce a single output. Since the output is processed by a majority voting circuit, even if one of the system fails there will be no errors in the output. In parity prediction, a parallel circuit (Hamming block) along with GF multiplier is used for error control. Hamming methodology corrects single error.
The rest of this paper is organized as follows. The algorithm for finite field multiplication over GF (2m) based on AOP is derived in Section II. In Section III, the error control using triple modular redundancy technique is discussed. In Section IV, the error detection and correction using parity prediction (Hamming) technique is discussed. In section V the triple modular redundancy and parity prediction techniques for AOP based GF multipliers are compared based on area, power consumption and delay. Finally the conclusion is given in Section VI.

ALGORITHM FOR GF MULTIPLIER
A polynomial of the form f(x) = f0 + f1x + f2x2 + f3x3 +
f4x4 + +fm xm over GF(2m) is called all one polynomial when f = 1 for i = 0,1,2,,m .
Ck =
According to the theorem quoted in [4], let y(x) = y0 + y1 + 2 y2 ++ m1 ym1 and Y(x) = Y0 + Y1 + 2 Y2 ++
i
For an all one polynomial to be irreducible (m+1)
m1 Ym1
+ m Ym
be the polynomials over GF(2m1) and
should be a prime number and 2 is the primitive modulo of (m+1). The values of m satisfying the above conditions for irreducibility (m 100) are 2, 4, 10, 12, 18, 28, 36, 52,
58, 60, 66, 82 and 100. The algorithm for AOP based GF multiplier is referred from [4] and [5].
Let be the root of f(x), then f() = 1 + + 2 + 3 + 4 + + m = 0. Therefore,
f() + f() = (1 + + 2 ++ m1 + m) + (1 +
+ 2 + 3 + 4 ++ m1 + m)
m+1 = 1 (1)
Since is the root of an irreducible all one polynomial of degree m. Any element y in the Galois field GF(2m) can be represented as y = y0 + y1 + 2y2 +..+ m1ym1 where yi GF(2) for (0 i m1) and {1, , 2, m1} forms the polynomial basis for degree m1.
Any element Y in the Galois field GF(2m) can also be represented as Y = Y0 + Y1 + 2Y2 ++ m1 Ym1 + m Ym where Yi GF(2) for (0 i m) and {1, , 2, m1, m } forms the extended polynomial basis for degree m.
The elements in Galois field a, b and c where c is the product of elements a and b, are represented in polynomial basis as,
a = a0 + a1 + a2 2 ++ am1 m1 (2)
b = b0 + b1 + b2 2 ++ bm1 m1 (3)
c = ab (mod f()) (4)
c = c0 + c1 + c2 2 ++ cm1 m1 (5)
where ai GF(2) for (0 i m1), bj GF(2) for (0 j
m1) and ck GF(2) for (0 k m1) and {1, , 2, m1} forms the canonical basis. The coefficients of c, ck can be calculated only by considering the extended polynomial basis representation. The algorithm is referred to [4].
The elements A, B are represented in extended polynomial basis as,
A = A0 + A1 + A2 2 + A3 3 ++ Am m (6)
B = B0 + B1 + B2 2 + B3 3 ++ Bm m (7)
where Ai GF(2) for (0 i m) and Bj GF(2) for (0 j m).
If C is the product of two elements A and B, then
C = AB mod [f ()] (8)
C = (A0 + A 1 ++ A m m)(B0 + B1 ++ B m m) [mod f()]
C = C0 + C1 + C2 2 + + Cm m (9)
where Ck GF(2) for (0 k m)
Therefore, the coefficient of C is given by,
GF(2m). If Y(x) and y(x) satisfy the condition, Y(x) = y(x) (mod p(x)), where p(x) is an AOP of degree m,then the coefficients of y(x) is given by yk = Yk Ym (mod 2) for (0
k m1)
Thus, the coefficients of c [= ab(mod f()], ck = Ck Cm for (0 k m1). The coefficient of c gives the final polynomial equation of GF multiplier output using AOP. The above algorithm is followed for the AOP based Galois field multiplier.

TRIPLE MODULAR REDUNDANCY
Triple Module Redundancy (TMR) is a very common fault tolerance technique. The principle of TMR is to triplicate the hardware circuit and a voter is added to the outputs. In this paper we are using TMR for standard AOP based GF multiplier. All standard AOP based GF multiplier are tripled and its respective outputs are connected to a voter. The voter will select the output of the majority of the modules. So, if one module fails, the error will not be reflected in the voter output.
A triple modular redundancy circuit with one voter for an all one polynomial based Galois field multiplier is shown in Fig.1.
Fig.1 TMR with single voter
a and b are the two inputs (each of four bits) given to the modules 1, 2 and 3. Each module is made up of All One Polynomial (AOP) based GF multiplier circuit. The output of each module is given simultaneously to the voter circuit.
Voter circuit does the majority gate function. It compares the output of the three modules and provides the majority output. In simple words, it performs the logical operation similar to the carry function derived in a full adder circuit. The Fig.2 explains the functionality of voter circuit.
Fig.2 Block diagram of single voter circuit
The voter performing carry logical function compares the output of each GF multiplier on a bitbybit basis and is given by,
Vc. Thus, even if an error occurs in any one of the voter, the output masks it and provides the correct output.
The advantage of TMR design is that it provides fault tolerance without the need for separate detection and recovery functions. TMR approach provides realtime masking of the faultymodule outputs. Also TMR is faster than the codebased techniques since the voting speed is independent of the information bit length because the voting is always performed on three bits.
o = x.y + y.z + x.z
where o is the output of voter and x, y and z are the outputs of modules 1, 2 and 3. Thus even if fault occurs in any one module, there occurs no error in final output.
Although in some cases the majority gate could fail or the systems with only one voter could itself fail. Due to this the complete system will fail. In order to mask this, voters can be tripled. Three voters are used, one for each copy of the next stage of TMR logic. In such systems there is no single point of failure.
The Fig.3 explains TMR with four voters. In order to avoid occurrence of error in a single voter circuit from the previous concept, three more voter circuits are added to it. Here each voter (V1, V2 and V3) compares the AOP multiplier output provided by three modules. The final fourth voter circuit (Va) chooses the majority voter output from V1, V2 and V3.
Fig.3 TMR with four voters
The Fig.4 explains TMR with seven voter circuit. To reduce the error occurrence caused by the voter circuit from the previous concept, three more voter circuits are added to it. The voter circuit V1, V2 and V3 gives the majority output of the modules. The majority output of voters V1, V2 and V3 is chosen by the voters Va, Vb and Vc. The final voter Vf gives the majority output of Va, Vb and
Fig.4 TMR with seven voters
The traditional TMR voter masks the faults affecting only one module and the faulty module cannot be recovered in a traditional TMR system, as the system cannot identify the faulty module.

PARITY PREDICTION
In this section the error detection and correction are done in a parallel manner by parity prediction technique where the parity bits are generated based on the Hamming principle. The Hamming codes are simplest of group of codes known as Linear Block codes. In Hamming, number of parity bits increases logarithmically with number of output bits which serves as the main advantage.
The principle difference between the Hamming codes which are used for memory application and our approach is that, instead of encoder we have parity prediction circuit and its size depends upon the number of input bits.
Parity prediction technique is one of the ways to detect errors. Since the parity prediction circuit runs in parallel with the multiplier, only the decoding and correction logic provides the delay penalty. The scheme is given in the Fig.5.
In the Galois Field multiplier block the standard AOP based GF multiplier is implemented. For a 4bit input (A and B), the GF multiplier output is of 4 bits c4, c3, c2 and c1. In the multiple parity prediction circuit block polynomial multiplication takes place i.e. the two 4 bit multiplier inputs are multiplied and the resultant 7 bits are stored as d0, d1, d2, d3, e0, e1 and e2.
Parity bits p0, p1, p2 are calculated from the standard multiplier output.
ps = d0 xor d1 xor d2 xor d3 xor e0 xor e1 xor e2 p0 = ps xor d2 xor e0
p1 = ps xor d1 xor e0 xor e2 p2 = ps xor d0 xor e1
The parity , are calculated from the GF multiplier output.
= c3 xor c4 xor c1
= c4 xor c2 xor c1
= c4 xor c3 xor c2
These two sets of parity bits are XORed to generate syndrome bits s0, s1 and s2.
If the syndrome bits are equal to zero then there is no error in the multiplication. If it is other than zero then the value denotes the position of the error.
The output of the Hamming block is formed from the syndrome bits.
p= (not s0) and s1 and (not s2) p = s0 and (not s1) and s2
p = s0 and (not s1) and s2 h4 = s0 and s1 and s2
The standard GF multiplier output and Hamming output are XORed to get the correct GF multiplication output.
Fig.5. Galois Field multiplier with parity prediction technique
(Hamming)

RESULTS AND DISCUSSION
The standard AOP based GF multiplier, Error control for AOP based GF multiplier using TMR techniques and Error detection and correction for AOP based Galois Field multiplier using parity prediction technique (Hamming)
are simulated to check the functionality. All the five GF multipliers (standard AOP, three TMR and parity prediction) are synthesized in FPGA and the results are tabulated in TABLE I
TABLE I COMPARISON OF ERROR CONTROL TECHNIQUES FOR AOP BASED GF MULTIPLIER
PARAMETERS
Standard AOP
based GF multiplier
TMR
with one voter
TMR
with four voters
TMR
with seven voters
Parity prediction (Hamming)
Gate count
84
234
732
2,226
207
Power(mW)
96
101
116
162
99
Delay(ns)
12.3
12.326
13.807
18.755
16.183
From the comparative results of gate count shown in Fig.6, it is seen that while using TMR techniques, a standard AOP based GF multiplier needs an additional increase of more than 175% of its actual area (i.e., area required by standard AOP multiplier), whereas parity prediction technique needs an area overhead of nearly 150%. Hence parity prediction requires less area than TMR.
Fig. 6 Comparison of gate count
From the comparative results of power consumption shown in Fig.7, it is seen that while using TMR techniques for a standard AOP based GF multiplier, an additional increase of more than 5% of its actual power (i.e., power consumed by standard AOP multiplier)is needed, while parity prediction technique consumes an additional power of nearly 3% of its actual power. Hence parity prediction techniquerequires less power than TMR.
From the comparative results of delay shown in Fig.8, it is seen that when standard AOP based GF multiplier uses parity prediction circuit there is an additional delay of 31%. While using TMR with less number of voters there is an increase in delay of only 212%. An increase in the number of voters in TMR increases the delay abruptly.
Fig. 7 Comparison of power consumption
Fig.8 Comparison of time delay

CONCLUSION
Thus all one polynomial based Galois field multiplier, error control using TMR techniques and parity prediction technique for GF multiplier are implemented using Xilinx ISE 8.1 and the results are compared. The comparative results of gate count proves that TMR techniques require an area overhead of more than 175%, while parity prediction technique requires an area overhead of only 150%. Similarly, power consumed by AOP based GF multiplier using TMR techniques are larger than the one with parity prediction technique. Hence, parity prediction technique (Hamming) is more efficient than Triple Modular Redundancy techniques for controlling error in AOP based GF multiplier considering the gate count and power as the important factors. TMR techniques lead to an increase in delay of 212% while parity prediction technique leads to an increase in delay of 31%. This can be taken up as a future work and the delay for parity prediction technique can be reduced.
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