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Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

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Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories. / Luo, Qiwu ; Fang, Xiaoxin; Sun, Yichuang; Ai, Jiaqiu; Yang, Chunhua.

In: IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 67, No. 3, 8949460, 03.01.2020, p. 939-950.

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@article{b0e424ac9e074293afce1e907d746fce,
title = "Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories",
abstract = "Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories. ",
keywords = "NAND flash memory, echo state network (ESN), hot data prediction, regularization, solid state disk (SSD)",
author = "Qiwu Luo and Xiaoxin Fang and Yichuang Sun and Jiaqiu Ai and Chunhua Yang",
note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ",
year = "2020",
month = jan,
day = "3",
doi = "10.1109/TCSI.2019.2960015",
language = "English",
volume = "67",
pages = "939--950",
journal = "IEEE Transactions on Circuits and Systems I: Regular Papers",
issn = "1549-8328",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

AU - Luo, Qiwu

AU - Fang, Xiaoxin

AU - Sun, Yichuang

AU - Ai, Jiaqiu

AU - Yang, Chunhua

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2020/1/3

Y1 - 2020/1/3

N2 - Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.

AB - Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.

KW - NAND flash memory

KW - echo state network (ESN)

KW - hot data prediction

KW - regularization

KW - solid state disk (SSD)

UR - http://www.scopus.com/inward/record.url?scp=85080900583&partnerID=8YFLogxK

U2 - 10.1109/TCSI.2019.2960015

DO - 10.1109/TCSI.2019.2960015

M3 - Article

VL - 67

SP - 939

EP - 950

JO - IEEE Transactions on Circuits and Systems I: Regular Papers

JF - IEEE Transactions on Circuits and Systems I: Regular Papers

SN - 1549-8328

IS - 3

M1 - 8949460

ER -