无冲突矢量有序就地基-r信念传播极化码译码算法

Arvid B. van den Brink, M. Bekooij
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引用次数: 3

摘要

向量化信念传播极化码解码器具有潜在的高吞吐量和集成能力,可以在处理器中进行向量化处理和访问宽存储字。然而,目前最先进的信念传播极化码解码器算法不执行矢量处理,并将中间结果存储在非连续的存储器位置。此外,目前最先进的信念传播极性码解码器需要单独的存储器来存储左边界和右边界的中间结果。本文提出了一种向量化的有序原地信念传播极化码解码器算法,该算法的所有阶段都从存储器中访问向量化的数据。这导致高吞吐量,因为元素的向量可以在每个时钟周期内从内存中取出并存储在内存中。我们的算法还适应每阶段就地计算,这将所需的内部内存减半。此外,该算法具有规则的内存地址访问模式。通过对一小组中间结果进行转置操作,实现无冲突的向量化内存访问。转置操作的使用还导致输入和输出结果都被放置在内存中的后续位置上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conflict-Free Vectorized In-order In-place Radix-r Belief Propagation Polar Code Decoder Algorithm
A vectorized belief propagation polar code decoder is desirable because of the potentially high throughput and the ability of integration in processors that perform vectorized processing and access wide memory words. However, current state-of-the-art belief propagation polar code decoder algorithms do not perform vector processing and store intermediate results in non consecutive memory locations. Also the current state-of-the-art belief propagation polar code decoders require separate memories to store left and right bound intermediate results. In this paper we propose a vectorized in-order in-place belief propagation polar code decoder algorithm where all stages access vectorized data from memory. This results in a high throughput because vectors of elements can be fetched from and stored in memory in each clock cycle. Our algorithm also accommodates for per stage in-place computations which halves the required internal memory. Furthermore, the algorithm has a regular memory addresses access pattern. Conflict free vectorized memory access is achieved by making use of transpose operations on small groups of intermediate results. The use of the transpose operations also results in that both input and output results are placed on subsequent locations in memory.
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