权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CIF:Small:Machine Learning Based Turbo Detection for Two and Three Dimensional Magnetic Recording

CIF：Small：基于机器学习的二维和三维磁记录 Turbo 检测

基本信息

批准号：
1817083
负责人：
Benjamin Belzer
金额：
$ 50万
依托单位：
Washington State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817083&HistoricalAwards=false
关键词：
CIF Small Machine Learning Based

项目摘要

This project investigates two and three dimensional magnetic recording (TDMR and 3DMR) for next generation hard disk drives. In TDMR, bits are written on two-dimensional patches of a magnetic storage disk, whereas in 3DMR bits are written on multiple disk layers. TDMR is an emerging technology that promises up to an order of magnitude increase in information bits per unit of disk area, without requiring radical redesign of the disk. 3DMR is an even newer technology that has the promise of significant areal information density increases over TDMR. A key problem in TDMR and 3DMR is that, at high densities, some bits are not written to any of the magnetic grains on the disk. Moreover, one must contend with signal dispersion: along-track, across-tracks, and between layers. This project investigates machine learning for turbo detection of TDMR and 3DMR channels at high densities of between 1 and 4 magnetic grains per coded bit. The considered machine learning topics include design of local area influence probabilistic model detectors, recently introduced by the investigators, and design of deep neural network detectors for TDMR and 3DMR. Through established collaborations, the investigators will validate the developed techniques with realistic waveforms and will facilitate technology transfer. The project also includes educational and outreach components. The investigators will work with the Voiland College of Engineering Diversity Programs office to identify potential undergraduate researchers from underrepresented groups to participate in the project.The specific technical objectives of this project are: (i) developing information-theoretic design techniques for deep neural networks, (ii) designing machine learning based media noise predictors for TDMR turbo-detectors, (iii) designing deep neural network detectors that handle both two-dimensional intersymbol interference and media noise, (iv) generalizing the machine learning based detector designs for 3DMR, and (v) evaluating the developed designs with TDMR and 3DMR waveforms derived from realistic micromagnetic simulations, obtained from international collaborators. This work is expected to provide significant progress toward the industry's information density target of 10 Terabits per square inch.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.

本项目研究用于下一代硬盘驱动器的二维和三维磁记录（TDMR和3DMR）。在TDMR中，位被写入磁存储盘的二维补丁上，而在3DMR中，位被写入多个盘层上。TDMR是一种新兴的技术，它承诺每单位磁盘面积的信息位数增加一个数量级，而不需要对磁盘进行根本性的重新设计。3DMR是一种甚至更新的技术，其具有比TDMR显著增加面信息密度的前景。TDMR和3DMR中的一个关键问题是，在高密度下，一些位没有写入磁盘上的任何磁性颗粒。此外，必须应对信号分散：沿轨道，跨轨道和层之间。该项目研究了TDMR和3DMR通道的Turbo检测的机器学习，其密度在每个编码位1到4个磁性颗粒之间。所考虑的机器学习主题包括研究人员最近引入的局部区域影响概率模型检测器的设计，以及TDMR和3DMR的深度神经网络检测器的设计。通过已建立的合作，研究人员将用逼真的波形验证所开发的技术，并将促进技术转让。该项目还包括教育和外联部分。调查人员将与Voiland工程学院多样性项目办公室合作，从代表性不足的群体中确定潜在的本科研究人员参与该项目。该项目的具体技术目标是：（i）开发用于深度神经网络的信息理论设计技术，（ii）为TDMR涡轮探测器设计基于机器学习的媒体噪声预测器，（iii）设计处理二维符号间干扰和媒体噪声的深度神经网络检测器，（iv）将基于机器学习的检测器设计推广到3DMR，以及（v）使用从国际合作者获得的真实微磁模拟中获得的TDMR和3DMR波形评估所开发的设计。这项工作预计将为业界实现每平方英寸10太比特的信息密度目标提供重大进展。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。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Turbo-Detection for Multilayer Magnetic Recording Using Deep Neural Network-Based Equalizer and Media Noise Predictor

使用基于深度神经网络的均衡器和介质噪声预测器进行多层磁记录的 Turbo 检测

DOI：
10.1109/tmag.2021.3122136
发表时间：
2022
期刊：
IEEE Transactions on Magnetics
影响因子：
2.1
作者：
Sayyafan, Amirhossein;Aboutaleb, Ahmed;Belzer, Benjamin J.;Sivakumar, Krishnamoorthy;Greaves, Simon;Chan, Kheong Sann
通讯作者：
Chan, Kheong Sann

Data Recovery for Multilayer Magnetic Recording

多层磁记录的数据恢复

DOI：
10.1109/tmag.2019.2937692
发表时间：
2019
期刊：
IEEE Transactions on Magnetics
影响因子：
2.1
作者：
Chan, Kheong Sann;Aboutaleb, Ahmed;Sivakumar, Krishnamoorthy;Belzer, Benjamin;Wood, Roger;Rahardja, Susanto
通讯作者：
Rahardja, Susanto

Deep Neural Network Media Noise Predictor Turbo-Detection System for 1-D and 2-D High-Density Magnetic Recording

DOI：
10.1109/tmag.2020.3038419
发表时间：
2020-08
期刊：
IEEE Transactions on Magnetics
影响因子：
2.1
作者：
Amirhossein Sayyafan;Ahmed Aboutaleb;B. Belzer;K. Sivakumar;Anthony Aguilar;Christopher A. Pinkham;K. Chan;Ashish James
通讯作者：
Amirhossein Sayyafan;Ahmed Aboutaleb;B. Belzer;K. Sivakumar;Anthony Aguilar;Christopher A. Pinkham;K. Chan;Ashish James

A perspective on deep neural network-based detection for multilayer magnetic recording

DOI：
10.1063/5.0051085
发表时间：
2021-07
期刊：
Applied Physics Letters
影响因子：
4
作者：
Ahmed Aboutaleb;Amirhossein Sayyafan;K. Sivakumar;B. Belzer;S. Greaves;K. Chan;R. Wood
通讯作者：
Ahmed Aboutaleb;Amirhossein Sayyafan;K. Sivakumar;B. Belzer;S. Greaves;K. Chan;R. Wood

Deep Neural Network-based Detection and Partial Response Equalization for Multilayer Magnetic Recording

基于深度神经网络的多层磁记录检测和部分响应均衡

DOI：
10.1109/tmrc49521.2020.9366719
发表时间：
2021
期刊：
2020 IEEE 31st Magnetic Recording Conference (TMRC
影响因子：
0
作者：
ABOUTALEB, Ahmed;SAYYAFAN, Amirhossein;BELZER, Benjamin;SIVAKUMAR, Krishnamoorthy;GREAVES, Simon;CHAN, Kheong;WOOD, Roger
通讯作者：
WOOD, Roger