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 (TDMR)，这是一种用于硬盘驱动器的新型记录技术，可大幅提高数据密度，高达每平方英寸 10 太比特。 TDMR 的优势来自两个方向，即 (i) 叠瓦写入机制，相邻数据磁道以部分重叠的方式写入，就像屋顶瓦一样，以便在磁盘上挤压更多磁道并增加数据密度，以及 (ii) 强大的功能信号处理算法能够在数据磁道内和数据磁道之间存在高水平干扰的情况下从嘈杂的读回信号中高效恢复数据。机器学习 (ML) 技术将用于在存在二维干扰、数据相关和有色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，最终允许将呈指数级增长的数据量存储在容量更大的更少磁盘驱动器上。该奖项部分支持一名博士生接受 TDMR 读取通道设计培训，最终为该学生在数据存储行业创造就业机会。研究目标是开发基于机器学习的高效均衡算法，该算法优于高密度 TDMR 的传统通信理论均衡。 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码率。所需的神经网络均衡方案寻求（i）结合媒体噪声的预测和消除，以及（ii）与研究者开发的新颖的读取通道架构兼容，该架构将部分响应范式扩展到以下情况异步轨道的多轨检测。为了实现这个读取通道，开发的均衡器将遵循旋转目标（ROTAR）算法，这是一种异步轨道的多轨道检测器，也是由研究人员开发的。由此产生的读取通道预计将比业界目前使用的通信理论和单轨检测方案在面密度和吞吐量方面产生增益。所开发算法的性能将与使用国际合作者提供的真实波形的传统算法进行比较。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。