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An Efficient Majority Error Detection in Logic Decoding with Euclidean Geometry Low Density Parity Check (Eg-Ldpc) Codes

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An Efficient Majority Error Detection in Logic Decoding with Euclidean Geometry Low Density Parity Check (Eg-Ldpc) Codes

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In a recent article, a technique was proposed to step up the majority logic decoding of variation set low density parity check codes. This is helpful as majority logic decoding can be implemented serially with trouble-free hardware but requires a huge decoding time. The method detect whether a word has errors in the initial iterations of majority logic decoding, and at what time there are no errors the decoding split ends without completing the rest of the iterations. While the majority words in a memory will be error-free, the average decoding time is significantly reduced. The outcome obtained proves that the technique is also effective for EG-LDPC codes. General simulation results are given to precisely estimate the prospect of error detection for different code sizes and numbers of errors.

In a recent article, a technique was proposed to step up the majority logic decoding of variation set low density parity check codes. This is helpful as majority logic decoding can be implemented serially with trouble-free hardware but requires a huge decoding time. The method detect whether a word has errors in the initial iterations of majority logic decoding, and at what time there are no errors the decoding split ends without completing the rest of the iterations. While the majority words in a memory will be error-free, the average decoding time is significantly reduced. The outcome obtained proves that the technique is also effective for EG-LDPC codes. General simulation results are given to precisely estimate the prospect of error detection for different code sizes and numbers of errors.

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An Efficient Majority Error Detection in Logic Decoding with Euclidean Geometry Low Density Parity Check (Eg-Ldpc) Codes

  1. 1. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.101-103 ISSN: 2278-2400 101 An Efficient Majority Error Detection in Logic Decoding with Euclidean Geometry Low Density Parity Check (Eg-Ldpc) Codes M .Vengadapathiraj, U.Maheswaran A.Karunakaran Assistant Professor,Department of Electronics & Communication Engineering,Rajalakshmi Institute of Technology, Chennai Abstract—In a recent article, a technique was proposed to step up the majority logic decoding of variation set low density parity check codes. This is helpful as majority logic decoding can be implemented serially with trouble-free hardware but requires a huge decoding time. The method detect whether a word has errors in the initial iterations of majority logic decoding, and at what time there are no errors the decoding split ends without completing the rest of the iterations. While the majority words in a memory will be error-free, the average decoding time is significantly reduced. The outcome obtained proves that the technique is also effective for EG-LDPC codes. General simulation results are given to precisely estimate the prospect of error detection for different code sizes and numbers of errors. Keywords— Euclidean geometry low-density parity check (EG-LDPC), Error correction codes, codes, majority logic decoding. I. INTRODUCTION Low-density parity-check (LDPC) codes form a different class of Shannon limit (or channel capacity)-approaching codes. Regrettably, Tanner's work was also unnoticed by coding theorists for another 14 years, until the late 1990s when some coding researchers began to examine codes on graphs and iterative decoding.Error correction codes are usually used to protect memories as of so-called elastic errors, which change the logical value of memory cells without harmful the circuit [1]. As expertise scales, memory devices turn out to be better and more powerful error correction codes are wanted [2], [3]. To this end, the use of more superior codes has been recently proposed [4]–[8]. These codes can correct a superior number of errors, but normally require complex decoders.To keep away from a high decoding difficulty, the use of one step majority logic Decodable codes be the first proposed in [4] for memory applications. Additional work on this topic was then accessible in [5], [6], [8]. One step majority logic decoding can be implemented in sequence with very simple circuitry [9], except require long decoding times. In a memory, this would increase the entrée time which is an significant system parameter [12, 18, 19]. Only a small amount of classes of codes can be decoded using one step majority logic decoding [9]. Among those is some Euclidean geometry low density parity check (EG-LDPC) codes which were used in [4], and disparity set low density parity check (DS-LDPC) codes [9].The large classes of finite-geometry LDPC codes have comparatively good minimum distances, and their Tanner graphs do not have short cycles [15]. These codes can be decoded in a range of ways ranging from low to high decoding difficulty and from sensibly good error performance to very good error performance. In addition [7], these codes are either cyclic or quasi-cyclic. Therefore, their encoding is easy and can be implemented by linear shift registers [23]. Some long finite-geometry LDPC codes contain error performance just a few tenths of a decibel away from the Shannon limit.A method was lately proposed in [10] to go faster a serial implementation of majority logic decoding of DS-LDPC codes. The design is rear the process is to use the first iterations of majority logic decoding to sense if the word being decoded contains errors. If there are no errors, then decoding can be congested without finishing the remaining iterations, so greatly dipping the decoding time. N K J tML 15 7 4 2 63 37 8 4 123 62 16 8 255 167 32 16 1023 765 64 32 Table 1: one step MLD EG-LDPC CODE Another benefit of the proposed technique is that it requires very little additional circuitry as the decoding circuitry is too used for error detection. For illustration, it was shown in [10] that the extra area required to implement the system was only around 1% for large word sizes.The method proposed in [10] relies on the properties of DS-LDPC codes and so it is not directly related to other code classes. In the following, a related approach for EG- LDPC codes is obtainable. The rest of this concise is divided into the following sections. Section II provide preliminary on EG-LDPC codes, majority logic decoding and the method proposed in [10]. Section III presents the results of Applying the scheme to EG-LDPC codes, include simulation results and a hypothesis based on those results [23]. This is complemented by a theoretical proof used for the cases of one and two errors that is provided in an Appendix.
  2. 2. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.101-103 ISSN: 2278-2400 102 II. EG-LDPC CODES This geometry consists of 2"'s point, and each point is representing by an m-tuple over GF (2'). The point represented with the all-zero in-tuple, 0 = (0, 0…. 0), is called the origin. There are lines in EG (in, 2s), and each line consists of 2' points. every point in EG (in, 2') is intersected bylines. Two lines in EG (in, 2s) are either disjointed or they cross at one and only point.To build a type-1 EG-LDPC code based on EG form the parity-check matrix whose rows are the incidence vectors of all the lines in EG and whose columns correspond to all the points in EG. Therefore, E• G consists of rows and o = 2"'s columns. Since every line in EG consists of 2' points, each row of FI (L) has weight p = 2s. Since each point in EG (in, 2') is intersect by (2"" – 1)/ (2" – 1) lines, every column of SEC have weight y = (2"'s – 1)/ (2" – 1). Fig. 1. Serial one-step majority logic decoder for the (15, 7) EG- LPDC code. The null space of 1 (E1G) therefore gives an LDPC code of length n = 2"'", which is call an in-dimensional type-1 (0, s) th- order EG-LDPC code, denoted by (m, 0, s). The least distance of this code is lesser bounded as follows: Because (2"1" – 1)/ (2" – 1) orthogonal check-sums can be shaped for each code hit, this code is proficient of correcting t Ai L = L (2"' -1)/2(2' -1)] or less arbitrary errors with one-step majority-logic decoding. N 1 error 2 error 3error 4 error 15 0 0 0 0 63 0 0 0 0 123 0 0 0 0 255 0 0 0 0 1023 0 0 0 0 Table 2: Undetected Errors in Exhaustive Checking If errors can be detected in the first some iterations of MLD, then when no errors are detected in those iterations, the decoding can be stopped without completing the respite of the iterations. In the first it- eration, errors will be detected when at least one of the check equations is precious by an odd number of bits in error[5,12,19]. In the second iteration, as bits are regularly shifted by one position, errors will involve other equations such that some errors undetected in the first iteration will be detected. As iterations go forward, all detectable errors will eventually be detected.Then the proposed system was implemented in VHDL and synthesized, showing that for codes with large block sizes the overhead is low. This is since the existing majority logic decoding circuitry is reused to do error detection and only a number of additional control logic is required. III. SIMULATION RESULTS The method proposed in [10] has been functional to the class of one step MLD EG-LDPC codes. To present the results, the conclusion is obtainable first in terms of a suggestion that is then validate by simulation and also partly by a theoretical study presented in the addendum. The results obtain can be summarized in the following hypothesis. “Given a word read as of a memory sheltered with one step MLD EG-LDPC codes, and affected by up to four bit-flips, all errors can be detected in only three decoding cycles”.Note that this theory is diverse from the one made for DS-LDPCs codes in [10] as in that case errors upsetting up to five bits were forever detected. This is owed to structural differences between DS-LDPC and EG-LDPC codes, [14 and 20] which will be detailed in the Appendix. To validate the over hypothesis, the EG-LDPC codes measured have been implemented and experienced.The results for the comprehensive checks are shown in Table 2. These results show the hypothesis for the codes with slighter word size (15 and 63). For up to three errors have been thoroughly tested while for only single and double error combination have been exhaustively testedThe simulation outcome presented proposes that all errors affecting three and four bits would be detected in the first three iterations. For errors affecting a better number of bits, there is a small likelihood of not being detected in those iterations [23]. For large word sizes, the probabilities are sufficiently small to be acceptable in many applications.In summary, the first three iterations will detect all errors moving four or less bits, and approximately every further detectable error affecting more bits. N 5 error 6 error 7error 8 error 9error 10 error 15 0 0 0 0 63 0 0 0 0 123 0 0 0 0 255 0 0 0 0 1023 0 0 0 0
  3. 3. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.101-103 ISSN: 2278-2400 103 Table3: Undetected Errors with one Billion random Error combinations This is a slightly inferior performance than in the case of DS- LDPC codes [10] where errors affecting five bits be adding- ally always detected. Though, the majority logic circuitry is simpler for EG-LDPC codes, as the amount of equations is a power of two and an approach base on sorting networks proposed in [8] can be used to decrease the rate of the majority logic voting. IV. CONCLUSION AND FUTURE WORK The detection of errors through the first iterations of sequential one step Majority Logic Decoding of EG-LDPC codes has been considered. The objective was to shrink the decoding time by stopping the decoding process when no errors are detected. The simulation outcome shows that all experienced combinations of errors upsetting up to four bits are detected in the first three iterations of decoding. These outcomes extend the ones lately accessible for DS-LDPC codes, making the modified one step popular logic decoding more attractive for memory applications. Future work includes extending the academic analysis to the cases of three and four errors. More normally, decisive the necessary number of iterations to detect errors affecting a given number of bits seems to be an interesting problem. A universal solution to that trouble would enable a fine-grained tradeoff between decoding time and error detection ability. REFERENCES [1] R. C. Baumann, “Radiation-induced soft errors in advanced semicon- ductor technologies,” IEEE Trans. Device Mater. Reliab., vol. 5, no. 3, pp. 301–316, Sep. 2005. [2] M. A. Bajura, Y. Boulghassoul, R. Naseer, S. DasGupta, A. F. Witulski, J. Sondeen, S. D. Stansberry, J. Draper, L. W. Massengill, and J. N.Damoulakis, “Models and algorithmic limits for an ECC-based ap- proach to hardening sub-100-nm SRAMs,” IEEE Trans. Nucl. Sci., vol.54, no. 4, pp. [3] R. Naseer and J. Draper, “DEC ECC design to improve memory reli- ability in sub-100 nm technologies,” Proc. IEEE ICECS, pp. 586– 589,2008. [4] S. Ghosh and P. D. Lincoln, “Dynamic low-density parity check codes for fault-tolerant nano-scale memory,” presented at the Foundations Nanosci. (FNANO), Snowbird, Utah, 2007. [5] S. Ghosh and P. D. Lincoln, “Low-density parity check codes for error correction in nanoscale memory,” SRI Computer Science Lab., MenloPark, CA, Tech. Rep. CSL-0703, 2007. [6] H. Naeimi and A. DeHon, “Fault secures encoder and decoder for memory applications,” in Proc. IEEE Int. Symp. Defect Fault Toler.VLSI Syst., 2007, pp. 409–417. [7] B. Vasic and S. K. Chilappagari, “An information theoretical frame- work for analysis and design of nanoscale fault-tolerant memories based on low-density parity-check codes,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 54, no. 11, pp. 2438–2446, Nov. 2007. [8] H. Naeimi and A. DeHon, “Fault secure encoder and decoder for nanomemory applications,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 17, no. 4, pp. 473–486, Apr. 2009. [9] S. Lin and D. J. Costello, Error Control Coding, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2004. [10] S. Liu, P. Reviriego, and J. Maestro, “Efficient majority logic fault detection with difference-set codes for memory applications,” IEEETrans. Very Large Scale Integr. (VLSI) Syst., vol. 20, no. 1, pp. 148–156, Jan. 2012. [11] H. Tang, J. Xu, S. Lin, and K. A. S. Abdel-Ghaffar, “Codes on finite geometries,” IEEE Trans. Inf. Theory, vol. 51, no. 2, pp. 572–596, Feb.2005. [12] Pedro Reviriego, Juan A. Maestro, and Mark F.Flanagan,” Error Detection in Majority Logic Decoding of Euclidean Geometry Low Density Parity Check (EG-LDPC) Codes” IEEE Trans. Very Large Scale Integration (VLSI) Systems,Vol. 21, No. 1, January 2013. [13] R. C. Baumann, “Radiation-induced soft errors in advanced semiconductor technologies,” IEEE Trans. Device Mater.Reliab., vol. 5, no. 3, pp. 301–316, Sep. 2005. [14] M. A. Bajura, Y. Boulghassoul, R. Naseer, S.DasGupta, A. F.Witulski, J. Sondeen, S. D. Stansberry, J. Draper, L. W.Massengill, and J. N.Damoulakis, “Models and algorithmic limits for an ECC-based approach to hardening sub-100-nm [15] SRAMs,” IEEE Trans. Nucl. Sci., vol. 54, no. 4, pp. 935–945, Aug. 2007. [16] R. Naseer and J. Draper, “DEC ECC design to improve memory reliability in sub-100 nm Technologies,” Proc. IEEE ICECS, pp. 586– 589, 2008. [17] S. Ghosh and P. D. Lincoln, “Dynamic low-density parity check codes for fault-tolerant nano-scale memory,” presented at the Foundations Nonsocial. (FNANO), Snowbird, Utah, 2007. [18] S. Ghosh and P. D. Lincoln, “Low-density parity check codes for error correction in nanoscale memory,” SRI Computer Science Lab., Menlo Park, CA, Tech. Rep. CSL-0703, 2007. [19] H. Naeimi and A. DeHon, “Fault secures encoder and decoder for memory applications,” in Proc. IEEE Int. Symp.Defect Fault Toler. VLSI Syst., 2007, pp. 409–417. [20] Pedro Reviriego, Juan A. Maestro, and Mark F. Flanagan “Error Detection in Majority Logic Decoding of Euclidean Geometry Low Density Parity Check (EG-LDPC) Codes” IEEE transactions on very large scale integration (vlsi) systems, vol. 21, no. 1, January 2013. [21] H. Naeimi and A. DeHon, “Fault secure encoder and decoder for nanomemory applications,” IEEE Trans. Very LargeScale Integr. (VLSI) Syst., vol. 17, no. 4, pp. 473–486, Apr. 2009. [22] S. Lin and D. J. Costello, Error Control Coding, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2004. [23] Pedro Reviriego, Juan A. Maestro, and Mark F. Flanagan “Error Detection in Majority Logic Decoding of Euclidean Geometry Low Density Parity Check (EG-LDPC) Codes” IEEE transactions on very large scale integration (vlsi) systems, vol. 21, no. 1, January 2013.
  4. 4. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.101-103 ISSN: 2278-2400 104

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