Suppr超能文献

用于低密度格码的定点解码器的硬件实现

Hardware Implementation of a Fixed-Point Decoder for Low-Density Lattice Codes.

作者信息

Srivastava Rachna, Gaudet Vincent C, Mitran Patrick

机构信息

Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W., Waterloo, ON N2L 3G1 Canada.

出版信息

J Signal Process Syst. 2022;94(1):101-116. doi: 10.1007/s11265-021-01735-2. Epub 2022 Jan 31.

Abstract

This paper describes a field-programmable gate array (FPGA) implementation of a fixed-point low-density lattice code (LDLC) decoder where the Gaussian mixture messages that are exchanged during the iterative decoding process are approximated to a single Gaussian. A detailed quantization study is first performed to find the minimum number of bits required for the fixed-point decoder to attain a frame error rate (FER) performance similar to floating-point. Then efficient numerical methods are devised to approximate the required non-linear functions. Finally, the paper presents a comparison of the performance of the different decoder architectures as well as a detailed analysis of the resource requirements and throughput trade-offs of the primary design blocks for the different architectures. A novel pipelined LDLC decoder architecture is proposed where resource re-utilization along with pipelining allows for a parallelism equivalent to 50 variable nodes on the target FPGA device. The pipelined architecture attains a throughput of 10.5 Msymbols/sec at a distance of 5 dB from capacity which is a 1.8 improvement in throughput compared to an implementation with 20 parallel variable nodes without pipelining. This implementation also achieves 24 improvement in throughput over a baseline serial decoder.

摘要

本文描述了一种定点低密度格码(LDLC)解码器的现场可编程门阵列(FPGA)实现方案,其中在迭代解码过程中交换的高斯混合消息被近似为单个高斯分布。首先进行了详细的量化研究,以找到定点解码器达到与浮点性能相似的误帧率(FER)所需的最少位数。然后设计了有效的数值方法来近似所需的非线性函数。最后,本文比较了不同解码器架构的性能,并详细分析了不同架构主要设计模块的资源需求和吞吐量权衡。提出了一种新颖的流水线式LDLC解码器架构,其中资源复用与流水线技术相结合,在目标FPGA器件上实现了相当于50个可变节点的并行度。该流水线架构在距离容量5 dB的情况下实现了10.5兆符号/秒的吞吐量,与无流水线的20个并行可变节点的实现相比,吞吐量提高了1.8倍。与基线串行解码器相比,该实现的吞吐量也提高了24倍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4616/8854306/a80b5a01240b/11265_2021_1735_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验