Data processing method and computer system medium thereof

• US 8,099,649 B2
• Filed: 06/06/2008
• Issued: 01/17/2012
• Est. Priority Date: 08/20/2007
• Status: Expired due to Fees

• Current Assignee: Lite-On Technology Corporation
• Original Assignee: Lite-On Technology Corporation
• Inventors: Hsu, Yueh-Teng
• Primary Examiner(s): Tabone, Jr., John J
• Application Number:
  US12/155,589
• Publication Number:
  US 20090055712A1
• Time in Patent Office: 1,320 Days
• Field of Search: 714/759, 714/781, 714/784, 714/785, 714/793, 714/808
• US Class Current: 714/759
• CPC Class Codes: H03M 13/158 Finite field arithmetic processing

Abstract

A data processing method includes the steps of: initializing a syndrome vector to be an (n−1)^th symbol; finding a corresponding mask based on the syndrome vector, wherein the mask is zero when the (n−1)^th symbol is zero; correcting a known constant, which is zero when the syndrome vector is zero, based on the mask; inputting the syndrome vector to a log look-up table to correspondingly find log data; performing a modulo addition operation corresponding to log maximum data to find a log sum based on the log data and a log known constant; and inputting the log sum to an anti-log look-up table to correspondingly find operational data.

Claims

1. A data processing method, comprising the steps of:

(a) initializing a syndrome vector to be an (n−

1)^th symbol;

(b) finding a corresponding mask based on the syndrome vector, wherein a value of the mask is equal to 0 when a value of the (n−

1)^th symbol is equal to 0;

(c) correcting a known constant based on the mask, wherein a value of the known constant corrected based on the mask is equal to 0 when a value of the syndrome vector is equal to 0;

(d) inputting the syndrome vector to a log look-up table to correspondingly find log data;

(e) performing, based on the log data and a log known constant, a modulo addition operation corresponding to log maximum data to find a log sum; and

(f) inputting the log sum to an anti-log look-up table to correspondingly find operational data.

2. The method according to claim 1, further comprising the steps of:

(g) summating the operational data and an (n−

2)^th symbol to find currently summated data;

(h) judging whether the (n−

2)^th symbol is a last symbol or not; and

(i) outputting the currently summated data as the syndrome vector when the (n−

2)^th symbol is the last symbol.

3. The method according to claim 2, wherein when the (n−

2)^th symbol is not the last symbol, the currently summated data serves as the syndrome vector and the steps (b) to (h) are repeatedly performed.

4. The method according to claim 1, wherein in the step (d), the log data having a value equal to 0 is corresponding found according to the log look-up table when the value of the syndrome vector is equal to 0.

5. The method according to claim 4, wherein the log data and the syndrome vector satisfy the equation:

S2=log(S1)+1,

wherein S1 and S2 are the syndrome vector inputted to the log look-up table and the log data corresponding found according to the log look-up table, respectively.

6. The method according to claim 1, wherein in the step (f), the operational data having a value equal to 0 is correspondingly found according to the anti-log look-up table when the log sum is equal to 1.

7. The method according to claim 1 being applied to a syndrome operation of a Reed-Solomon code.

8. The method according to claim 1, wherein in the step (b), the mask is equal to a maximum data value when the syndrome vector is not equal to 0.

9. The method according to claim 1, wherein in the step (c), the known constant is corrected by performing an AND operation according to the mask and the known constant.

10. A computer system medium having a computer-executable instruction for performing a data processing method, the data processing method comprising the steps of:

(a) initializing a syndrome vector to be an (n−

1)^th symbol;

(b) finding a corresponding mask according to the syndrome vector, wherein a value of the mask is equal to 0 when a value of the (n−

1)^th symbol is equal to 0;

(c) correcting a known constant based on the mask, wherein a value of the known constant corrected based on the mask is equal to 0 when a value of the syndrome vector is equal to 0;

(d) inputting the syndrome vector to a log look-up table to correspondingly find log data;

(e) performing, based on the log data and a log known constant, a modulo addition operation corresponding to log maximum data to find a log sum; and

(f) inputting the log sum to an anti-log look-up table to correspondingly find operational data.

11. The medium according to claim 10, wherein the data processing method further comprises the steps of:

(g) summating the operational data and an (n−

2)^th symbol to find currently summated data;

(h) judging whether the (n−

2)^th symbol is a last symbol or not; and

(i) outputting the currently summated data as the syndrome vector when the (n−

2)^th symbol is the last symbol.

12. The medium according to claim 11, wherein when the (n−

2)^th symbol is not the last symbol, the currently summated data serves as the syndrome vector and the steps (b) to (h) are repeatedly performed.

13. The medium according to claim 10, wherein in the step (d), the log data having a value equal to 0 is corresponding found according to the log look-up table when the value of the syndrome vector is equal to 0.

14. The medium according to claim 13, wherein the log data and the syndrome vector satisfy the equation:

S2=log(S1)+1,

wherein S1 and S2 are the syndrome vector inputted to the log look-up table and the log data corresponding found according to the log look-up table, respectively.

15. The medium according to claim 10, wherein in the step (f), the operational data having a value equal to 0 is correspondingly found according to the anti-log look-up table when the log sum is equal to 1.

16. The medium according to claim 10, wherein the data processing method is applied to a syndrome operation of a Reed-Solomon code.

17. The medium according to claim 10, wherein in the step (b) of the data processing method, the mask is equal to a maximum data value when the syndrome vector is not equal to 0.

18. The medium according to claim 10, wherein in the step (c), the known constant is corrected by performing an AND operation according to the mask and the known constant.

19. The medium according to claim 10 being applied to a computer system having a vectorization parallel processing instruction set, wherein the computer system performs the steps (a) to (f) of the data processing method on the symbols in response to instructions of the vectorization parallel processing instruction set.