Design of Low-Latency Layered Normalized Minimum Sum Low-Density Parity-Check Decoding Based on Entropy Feature for...

As high-speed big-data communications impose new requirements on storage latency, low-density parity-check (LDPC) codes have become a widely used technology in flash-memory channels. However, the iterative LDPC decoding algorithm faces a high decoding latency problem due to its mechanism based on iterative message transmission. Motivated by the unbalanced bit reliability of codeword, this paper proposes two technologies, i.e., serial entropy feature-based layered normalized min-sum (S-EFB-LNMS) decoding and parallel entropy feature-based layered normalized min-sum (P-EFB-LNMS) decoding. First, we construct an entropy feature vector that reflects the real-time bit reliability of the codeword. Then, the reliability of the output information of the layered processing unit (LPU) is evaluated by analyzing the similarity between the check matrix and the entropy feature vector. Based on this evaluation, we can dynamically allocate and schedule LPUs during the decoding iteration process, thereby optimizing the entire decoding process. Experimental results show that these techniques can significantly reduce decoding latency.