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Convergence-optimized variable node structure for stochastic LDPC decoder
Conference paper

Convergence-optimized variable node structure for stochastic LDPC decoder

Qichen Zhang, Yun Chen, Di Wu, Xiaoyang Zeng and Yeong-Luh Ueng
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol.2016-May, pp.6535-6539
18/05/2016

Abstract

convergence-optimized LDPC decoder stochastic decoding up/down counter
By using stochastic computation, a fully-parallel low-density parity-check (LDPC) decoder can be implemented using a lower wire complexity. In order to enhance the decoder performance, probability tracers, such as up/down counters, are added at each edge between variable nodes and check nodes, as described in previous literature. However, this causes a large decoding latency and a high number of decoding failures. In this paper, a convergence-optimized structure for variable nodes is proposed that is able to overcome these issues. As a result, the throughput for the proposed decoder is 20.5Gb/s, which is 101% higher than the original counter-based decoder presented in the previous literature.

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