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A 588 Gbps LDPC Decoder Based on Finite-Alphabet Message Passing

Hardware Architecture 2018-01-03 v2 Information Theory math.IT

Abstract

An ultra-high throughput low-density parity check (LDPC) decoder with an unrolled full-parallel architecture is proposed, which achieves the highest decoding throughput compared to previously reported LDPC decoders in the literature. The decoder benefits from a serial message-transfer approach between the decoding stages to alleviate the well-known routing congestion problem in parallel LDPC decoders. Furthermore, a finite-alphabet message passing algorithm is employed to replace the variable node update rule of the standard min-sum decoder with look-up tables, which are designed in a way that maximizes the mutual information between decoding messages. The proposed algorithm results in an architecture with reduced bit-width messages, leading to a significantly higher decoding throughput and to a lower area as compared to a min-sum decoder when serial message-transfer is used. The architecture is placed and routed for the standard min-sum reference decoder and for the proposed finite-alphabet decoder using a custom pseudo-hierarchical backend design strategy to further alleviate routing congestions and to handle the large design. Post-layout results show that the finite-alphabet decoder with the serial message-transfer architecture achieves a throughput as large as 588 Gbps with an area of 16.2 mm2^2 and dissipates an average power of 22.7 pJ per decoded bit in a 28 nm FD-SOI library. Compared to the reference min-sum decoder, this corresponds to 3.1 times smaller area and 2 times better energy efficiency.

Keywords

Cite

@article{arxiv.1703.05769,
  title  = {A 588 Gbps LDPC Decoder Based on Finite-Alphabet Message Passing},
  author = {Reza Ghanaatian and Alexios Balatsoukas-Stimming and Christoph Muller and Michael Meidlinger and Gerald Matz and Adam Teman and Andreas Burg},
  journal= {arXiv preprint arXiv:1703.05769},
  year   = {2018}
}

Comments

Published in IEEE Transactions on Very Large Scale Integration (TVLSI) Systems, Nov. 2017

R2 v1 2026-06-22T18:48:07.350Z