CRII: CIF: Machine Learning Based Equalization Towards Multitrack Synchronization and Detection in Two-Dimensional Magnetic Recording

CRII：CIF：基于机器学习的均衡，实现二维磁记录中的多轨同步和检测

基本信息

批准号：
2105092
负责人：
Elnaz Banan Sadeghian
金额：
$ 17.5万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105092&HistoricalAwards=false
关键词：
CRII CIF Machine Learning Based

项目摘要

This project concerns two-dimensional magnetic recording (TDMR), a novel recording technology for hard disk drives that allows for a drastic increase in data density, up to 10 terabits per square inch. Gains from TDMR come from two directions, namely (i) the shingled writing mechanism whereby adjacent data tracks are written with partial overlap, like roof shingles, in order to squeeze many more tracks on the disk and increase data density, and (ii) powerful signal processing algorithms that enable efficient data recovery from noisy readback signals in the presence of high levels of interference both within and across data tracks. Techniques from machine learning (ML) will be used in developing such data recovery algorithms in the presence of two-dimensional interference, and data-dependent and colored media noise. The proposed work aims to achieve significant improvements in TDMR, eventually allowing exponentially increasing volumes of data to be stored on fewer disk drives with higher capacities. This award partially supports a PhD student to be trained in TDMR read channel design, ultimately creating career opportunities for the student in the data storage industry. The research objective is the development of efficient ML based equalization algorithms that outperform conventional communication-theoretic equalization for high density TDMR. The TDMR channel being highly nonlinear, ML approaches are expected to better learn its characteristics, potentially leading to higher bit-error rates when compared to conventional linear communication-theoretic schemes. The desired neural network equalization schemes seek to (i) incorporate the prediction and cancellation of the media noise, and (ii) be compatible with a novel read channel architecture, developed by the investigator, that extends the partial-response paradigm to the case of multitrack detection of asynchronous tracks. To realize this read channel, the developed equalizers will be followed by the rotating-target (ROTAR) algorithm, a multitrack detector of asynchronous tracks, also developed by the investigator. The resulting read channel is expected to yield gains in areal density and throughput over the communication-theoretic and single-track detection schemes currently used in the industry. The performance of the developed algorithms will be compared against that of conventional algorithms using realistic waveforms provided by international collaborators.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.

该项目涉及二维磁性记录（TDMR），这是一种针对硬盘驱动器的新型记录技术，可大大增加数据密度，每平方英寸高达10吨。 TDMR的收益来自两个方向，即（i）包装的写作机制，邻近的数据轨道以部分重叠（例如屋顶木瓦）编写，以便在磁盘上挤压更多的轨道并增加数据密度，以及（ii）有效的信号处理算法，从而有效地恢复了噪声的数据轨道，从而使数据恢复了较高的数据，并在较高的范围内进行了级别的影响。机器学习（ML）的技术将用于在存在二维干扰以及数据依赖于数据的媒体噪声的情况下开发此类数据恢复算法。拟议的工作旨在实现TDMR的重大改进，最终允许将数据量增加存储在更少的磁盘驱动器上，具有较高的能力。该奖项部分支持了将在TDMR阅读频道设计中接受培训的博士生，最终为数据存储行业的学生创造了职业机会。研究目标是开发基于高度密度TDMR的常规通信理论均衡的有效均衡算法。 TDMR通道是高度非线性的，ML方法有望更好地学习其特征，与传统的线性通信理论方案相比，可能会导致较高的比特率。所需的神经网络均衡方案寻求（i）将介质噪声的预测和取消结合在一起，并且（ii）与研究者开发的新型读取通道结构兼容，该研究者将部分响应范式扩展到了异步轨迹的多曲线检测案例。为了实现此读取通道，开发的均衡器将随后是旋转目标（旋转）算法，这是异步轨道的多站检测器，也是由研究者开发的。预计所得的读取渠道将在该行业目前使用的通信理论和单轨检测方案上产生面积密度和吞吐量的增长。将使用国际合作者提供的现实波形将开发算法的性能与传统算法的性能进行比较。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。