Cell-wise encoding and decoding for TLC flash memories

Daniel Nicolas Bailon, S. Shavgulidze, J. Freudenberger
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引用次数: 1

Abstract

Automotive computing applications like AI databases, ADAS, and advanced infotainment systems have a huge need for persistent memory. This trend requires NAND flash memories designed for extreme automotive environments. However, the error probability of NAND flash memories has increased in recent years due to higher memory density and production tolerances. Hence, strong error correction coding is needed to meet automotive storage requirements. Many errors can be corrected by soft decoding algorithms. However, soft decoding is very resource-intensive and should be avoided when possible. NAND flash memories are organized in pages, and the error correction codes are usually encoded page-wise to reduce the latency of random reads. This page-wise encoding does not reach the maximum achievable capacity. Reading soft information increases the channel capacity but at the cost of higher latency and power consumption. In this work, we consider cell-wise encoding, which also increases the capacity compared to page-wise encoding. We analyze the cell-wise processing of data in triple-level cell (TLC) NAND flash and show the performance gain when using Low-Density Parity-Check (LDPC) codes. In addition, we investigate a coding approach with page-wise encoding and cell-wise reading.
TLC闪存的Cell-wise编码和解码
像人工智能数据库、ADAS和高级信息娱乐系统这样的汽车计算应用对持久内存有着巨大的需求。这种趋势需要为极端汽车环境设计的NAND闪存。然而,近年来,由于更高的存储密度和生产公差,NAND闪存的错误概率有所增加。因此,需要强纠错编码来满足汽车存储的要求。软译码算法可以纠正许多错误。然而,软解码是非常资源密集的,应该尽可能避免。NAND闪存是按页组织的,纠错码通常按页编码,以减少随机读取的延迟。此分页编码未达到可实现的最大容量。读取软信息增加了信道容量,但代价是更高的延迟和功耗。在这项工作中,我们考虑了单元智能编码,与页面智能编码相比,它也增加了容量。我们分析了三电平单元(TLC) NAND闪存中逐单元的数据处理,并展示了使用低密度奇偶校验(LDPC)代码时的性能增益。此外,我们研究了一种基于页面的编码和基于单元的读取的编码方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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