University of Hertfordshire

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

Research output: Contribution to journalArticlepeer-review


  • 08949460

    Accepted author manuscript, 3.64 MB, PDF document

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Original languageEnglish
Article number8949460
Pages (from-to)939-950
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Early online date3 Jan 2020
Publication statusE-pub ahead of print - 3 Jan 2020


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.


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