2022 IEEE 33rd Magnetic Recording Conference (TMRC)最新文献

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Hysteresis loops of recording media grains under the influence of high frequency fields 高频场作用下记录介质颗粒的磁滞回线
2022 IEEE 33rd Magnetic Recording Conference (TMRC) Pub Date : 2022-08-01 DOI: 10.1109/tmrc56419.2022.9918539
S. Greaves, Y. Kanai
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引用次数: 0
Neural Network Equalization for Asynchronous Multitrack Detection in TDMR TDMR中异步多轨检测的神经网络均衡
2022 IEEE 33rd Magnetic Recording Conference (TMRC) Pub Date : 2022-07-06 DOI: 10.1109/TMRC56419.2022.9918163
E. Sadeghian
{"title":"Neural Network Equalization for Asynchronous Multitrack Detection in TDMR","authors":"E. Sadeghian","doi":"10.1109/TMRC56419.2022.9918163","DOIUrl":"https://doi.org/10.1109/TMRC56419.2022.9918163","url":null,"abstract":"The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly detect multiple asynchronous tracks. In this paper, we propose to replace the conventional communication-theoretic multiple-input multiple-output equalizer in the GPRML architecture with a neural network equalizer for better adaption to the nonlinearity of the underlying channel. We evaluate the proposed equalization strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed equalizer outperforms the conventional linear equalizer, by a 37% reduction in the bit-error rate and a 33% gain in the areal density.","PeriodicalId":432413,"journal":{"name":"2022 IEEE 33rd Magnetic Recording Conference (TMRC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions 二值神经网络在无源磁隧道结阵列上的实现
2022 IEEE 33rd Magnetic Recording Conference (TMRC) Pub Date : 2021-12-16 DOI: 10.1109/TMRC56419.2022.9918590
Jonathan M. Goodwill, N. Prasad, B. Hoskins, M. Daniels, A. Madhavan, L. Wan, T. Santos, M. Tran, J. Katine, P. Braganca, M. Stiles, J. McClelland
{"title":"Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions","authors":"Jonathan M. Goodwill, N. Prasad, B. Hoskins, M. Daniels, A. Madhavan, L. Wan, T. Santos, M. Tran, J. Katine, P. Braganca, M. Stiles, J. McClelland","doi":"10.1109/TMRC56419.2022.9918590","DOIUrl":"https://doi.org/10.1109/TMRC56419.2022.9918590","url":null,"abstract":"Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, non-volatility, and scalability. However, in hardware realizations, device variations, write errors, and parasitic resistance degrade performance. To quantify such effects, we perform inference experiments on a 2-layer perceptron constructed from a 15 x 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve median accuracy of 95.3% with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.","PeriodicalId":432413,"journal":{"name":"2022 IEEE 33rd Magnetic Recording Conference (TMRC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133299248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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