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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.

该项目涉及二维磁记录，这是一种用于硬盘驱动器的新型记录技术，可大幅增加数据密度，最高可达每平方英寸10太比特。来自TDMR的收益来自两个方向，即（i）叠瓦式写入机制，其中相邻的数据轨道以部分重叠的方式写入，如屋顶瓦，以便在盘上挤压更多的轨道并增加数据密度，以及（ii）强大的信号处理算法，其使得能够在数据轨道内和跨数据轨道存在高水平干扰的情况下从噪声读回信号中有效地恢复数据。来自机器学习（ML）的技术将用于在存在二维干扰、数据相关和有色介质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的显着改进，最终允许将呈指数级增加的数据量存储在更少、容量更高的磁盘驱动器上。该奖项部分支持博士生接受TDMR读取通道设计培训，最终为数据存储行业的学生创造就业机会。研究目标是开发高效的ML为基础的均衡算法，优于传统的通信理论均衡高密度TDMR。TDMR信道是高度非线性的，ML方法有望更好地学习其特性，与传统的线性通信理论方案相比，可能会导致更高的误比特率。所需的神经网络均衡方案寻求（i）将媒体噪声的预测和消除，以及（ii）与一种新的读取通道架构兼容，由研究者开发，该架构将部分响应范例扩展到异步轨道的多轨道检测的情况。为了实现这一读取通道，开发的均衡器将遵循的搜索目标（ROTAR）算法，异步轨道的多轨探测器，也由调查员开发。由此产生的读取通道，预计将产生增益的面密度和吞吐量的通信理论和单轨检测方案目前在行业中使用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。