Name
Title | ||
|---|---|---|
Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories |
Abstract | ||
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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 L
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and L
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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
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recent state-of-the-art methods. Finally, simulation results about
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typical metrics tested on
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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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSI.2019.2960015 | IEEE Transactions on Circuits and Systems I: Regular Papers |
Keywords | DocType | Volume |
NAND flash memory,solid state disk (SSD),echo state network (ESN),hot data prediction,regularization | Journal | 67 |
Issue | ISSN | Citations |
3 | 1549-8328 | 2 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiwu Luo | 1 | 14 | 9.06 |
| Xiaoxin Fang | 2 | 8 | 1.85 |
| Yichuang Sun | 3 | 225 | 60.38 |
| Jiaqiu Ai | 4 | 2 | 0.36 |
| Chunhua Yang | 5 | 435 | 71.63 |