Small-area parallel syndrome calculation for strong BCH decoding

This paper presents a new optimization method to reduce the hardware complexity of syndrome calculation in strong BCH decoding. All the operations required in the parallel syndrome calculation are reformulated as a single matrix computation to enlarge the search area for common sub-expressions. The computational complexity of syndrome calculation is significantly reduced by finding and sharing common terms in the single matrix computation. Implementation results show that the proposed architecture saves 55% of area overheads compared to the conventional structure.