A Machine Learning-Based Design for an Efficient and Reliable NAND Flash Memory Storage Systems

• Main Authors: Huan-Ting Chen, 陳奐廷
• Other Authors: Chin-Hsien Wu
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/u9u85c

碩士 === 國立臺灣科技大學 === 電子工程系 === 106 === 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.