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GPU-BASED IMPLEMENTATION OF BELIEF PROPAGATION DECODING FOR POLAR CODES

Citation Author(s):
Zhanxian Liu, Rongke Liu, Zhiyuan Yan, Ling Zhao
Submitted by:
zhanxian liu
Last updated:
8 May 2019 - 1:19am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Zhiyuan Yan
Paper Code:
2164
 

Belief Propagation (BP) decoding provides soft outputs and features high-level parallelism. In this paper, we propose an optimized software BP decoder for polar codes on graphics processing units (GPUs). A full-parallel decoding architecture for codes with length n ≤ 2048 is presented to simultaneously update n/2 processing elements (PEs) within each stage and achieve high on-chip memory utilization by using
8-bit quantization. And, for codes with length n > 2048, a partial-parallel decoding architecture is proposed to partly update PEs of each stage in parallel and coalesced global memory accesses are performed. Experimental results show that, with incorporation of the G-matrix based early termination criterion, more than 1 Gbps throughput for codes n ≤ 1024 can be achieved on NVIDIA TITAN Xp at 5 dB while the
decoding latency is less than 1 ms. Compared with the state-of-the-art works, the proposed decoder achieves throughput speedups from 2.59x to 131x and provides good tradeoff between error performance and throughput.

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