ECCS/CCSS: Neural Network Nonlinear Iterative LDPC Decoders with Guaranteed Error Performance and Fast Convergence

ECCS/CCSS：具有保证错误性能和快速收敛的神经网络非线性迭代 LDPC 解码器

基本信息

批准号：
2027844
负责人：
Bane Vasic
金额：
$ 32万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027844&HistoricalAwards=false
关键词：
ECCS CCSS Neural Network Nonlinear

项目摘要

Low-density parity-check (LDPC) codes are an integral part of modern communications and data storage systems. In many applications, achieving desired data reliability with strict throughput, latency, area, energy or power constraints is challenging, and thus there is an unmet need for fundamental breakthroughs in decoding algorithms. To meet this need, the research team will develop novel sophisticated decoding algorithms for LDPC codes. The new decoders will have a direct application in flash memories and optical communications which have the most stringent requirements on error performance, and in massive machine-type communications requiring very low-latencies. The proposed research will create a path to a new generation of chips with greatly reduced energy requirements and lower environmental footprint. The participating students will receive advanced training in engineering and mathematics. Their educational experiences will be enriched by close collaboration between the PI and his national and international collaborators from academia and industry.The conventional LDPC decoding algorithms operate on a graphical model of a code by propagating messages along edges of the graph. In such an iterative message-passing decoder, the memory needed to store messages is proportional to the product of the message width and the number of nodes in the graph, which is of the order of thousands for codes of practical interest. Therefore lowering message width reduces the complexity greatly, but causes a degradation of decoding performance known as error floor, and slows down decoding convergence. The proposed research addresses the hardware complexity, error floor and convergence problems in a unified way by the following novel approaches: (1) Nonlinear message update functions â€“ Error floor is directly linked to the presence of certain subgraph structures, called trapping sets, in the Tanner graph that induce decoder failures. Our message update rules will be nonlinear and judiciously chosen to eliminate the effect of trapping sets, and will thus lower the error floor. (2) Guaranteed error correction capability â€“ Moreover, the precise knowledge of what trapping sets are not correctable by a decoder solves the fundamental issue associated with LDPC codes - the lack of data reliability guarantees. This opens a plethora of design possibilities, but since the number of potentially good message update rules is large, a systematic method is needed to search for the optimal ones, and for this we will rely on neural networks. (3) Neural network decoders â€“ The main idea follows from the observation that a graph unwrapped in time (iterations) essentially forms a neural network which can be trained to produce an optimal update rule. The resulting update rule also requires much smaller number of iterations for a successful decoding. (4) Built-in learning â€“ The decoders will be equipped with instruments of learning. In this way, the gradient descent algorithm is not used offline during training but during decoding. The loss function based on the concept of energy function from statistical mechanics, and captures the distance of the decoder output from a codeword through the number of unsatisfied parity checks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

低密度平价检查（LDPC）代码是现代通信和数据存储系统的组成部分。在许多应用中，挑战了严格的吞吐量，潜伏期，区域，能量或功率约束，实现所需的数据可靠性，因此在解码算法中没有基本突破的需求。为了满足这一需求，研究团队将开发新颖的，用于LDPC代码的复杂解码算法。新解码器将在闪存记忆和光学通信中具有直接的应用程序，这些应用程序对错误性能的要求最严格，并且在需要非常低范围的大型机器类型通信中。拟议的研究将为新一代筹码提供大幅减少能源需求和降低环境足迹的途径。参与的学生将接受工程和数学的高级培训。 PI与他的国家和国际合作者在学术界和行业之间的密切合作之间的密切合作将使他们的教育经历丰富。传统的LDPC解码算法通过沿图的边缘传播消息来以代码的图形模型运行。在这样的迭代消息解码器中，存储消息所需的内存与消息宽度的乘积和图中的节点的数量成正比，该节点的数量是数千个用于实践意义的代码的顺序。因此，降低消息宽度可降低复杂性的巨大，但会导致解码性能的降低称为误差地板，并减慢解码收敛。拟议的研究通过以下新颖的方法以统一的方式解决了硬件的复杂性，错误地板和收敛问题：（1）非线性消息更新功能 - 错误地面与某些子绘图结构的存在直接链接，称为陷阱集合，在Tanner图中，在Tanner图中影响解析规则。因此，我们的消息更新规则会较低地构建通道，以较低的方式逐渐限制效果，并消除效果的效果，并逐渐效法效果，并逐步效仿。（2）保证错误校正能力 - 此外，解码器无法纠正哪些捕获集的精确知识解决了与LDPC代码相关的基本问题 - 缺乏数据可靠性保证。这打开了大量的设计可能性，但是由于潜在的好消息更新规则的数量很大，因此需要系统的方法来搜索最佳方法，为此，我们将依靠神经网络。（3）神经网络解码 - 主要思想得出的观察结果是，图表在时间上解开（迭代）基本上形成了一个神经元网络，可以培训该网络以制定最佳更新规则。所得更新规则还需要较小的迭代来成功解码。这样，在训练期间而是在解码过程中，梯度下降算法不会离线使用。基于统计力学的能量函数概念的损失功能，并通过不满意的奇偶校验检查捕获了解码器输出与代码字的距离。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准通过评估来评估的。