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研究生:陳奐廷
研究生(外文):Huan-Ting Chen
論文名稱:基於機器學習的高效能與具可靠性的快閃記憶體儲存系統設計
論文名稱(外文):A Machine Learning-Based Design for an Efficient and Reliable NAND Flash Memory Storage Systems
指導教授:吳晋賢
指導教授(外文):Chin-Hsien Wu
口試委員:陳雅淑謝仁偉張經略
口試委員(外文):Ya-Shu ChenJen-Wei HsiehChing-Lueh Chang
口試日期:2018-07-27
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:53
中文關鍵詞:快閃記憶體轉換層機器學習快閃記憶體冷熱資料分類
外文關鍵詞:Flash Translation LayerMachine LearningFlash Memoryhot cold data classification
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NAND Flash Memory具有體積小、非揮發性、抗震性高、功耗較低以及存取速度快等優點,隨著技術的進步,NAND Flash Memory已從SLC (Single-Level Cell)發展至容量更大的MLC (Multi-Level Cell)及TLC (Triple-Level Cell),讓單位體積的資訊儲存量大幅增加,因此近年來NAND Flash Memory廣泛使用在各類電腦、行動裝置、嵌入式裝置或大型儲存系統上。然而NAND Flash Memory仍然存在硬體的限制,NAND Flash Memory中的區塊 (block) 有抹除次數 (Erase Count) 的上限,當抹除次數到達上限後,會有壞掉的風險,且NAND Flash Memory並不支援覆寫(Overwrite)的功能,要抹除一個Block之前,必須將Block中所有有效的(Valid)頁面 (Page) 都複製並且搬移。因此許多關於NAND Flash Memory的快閃記憶體轉換層 (Flash Translation Layer, FTL) 被提出以管理資料位置、減少資料搬移, FTL在存放資料時,若能分類冷、熱資料,在抹除Block 時,挑選的熱資料多的Block會因熱資料易改動為無效(Invalid)的狀態,而達到減少資料搬移。本文運用Machine Learning 演算法改進現有的FTL,並藉由區分冷、熱資料來減少資料搬移、降低抹除次數並提升NAND Flash Memory壽命。
NAND Flash Memory has the advantages of small size, non-volatile, high shock resistance, low power consumption and fast access speed. With the advancement of technology, NAND Flash Memory has evolved from SLC (Single-Level Cell) to larger capacity. MLC (Multi-Level Cell) and TLC (Triple-Level Cell) increase the storage density. Therefore, NAND Flash Memory has been widely used in various computers, mobile devices, embedded devices or large storage systems in recent years. However, NAND Flash Memory still has hardware limitations. Blocks in NAND Flash Memory have a limit of Erase Count. When the number of erases reaches the limit, there is a risk of Data loss.Also, NAND Flash Memory does not support the overwrite function. Before you can erase a block, you must copy and move all valid pages in the block. Therefore, many flash translation layers (FTL) for NAND Flash Memory are proposed to manage data location and reduce data movement. If FTL can separate cold and hot data, when garbage collection started, choose the hot block can reduce live page copy. This paper uses the Machine Learning algorithm to improve the existing FTL, and to reduce live page copy, and improve NAND Flash Memory life by separating cold and hot data.
第一章緒論 1
1.1. 前言 1
1.2. 論文架構 5
第二章環境背景和研究動機 6
2.1 Flash Translation Layer 6
2.2. Hot-Cold 12
2.3. 研究動機 14
第三章研究方法 15
3.1. 整體架構(Overall Structure) 15
3.2. MLBD分類器的訓練 16
3.2.1. 資料分群 (Data Clustering) 16
3.2.2. 訓練資料分類器 20
3.3. MLBD運作方式 26
第四章實驗與效能分析 31
4.1. 實驗環境 31
4.2. 實驗概述 33
4.3. 實驗結果分析 34
4.3.1. 實驗結果 34
4.3.2. 寫入放大率 41
第五章結論 43
第六章參考文獻 44
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