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Cost-Effective Reliable MLC PCM Architecture Using Virtual Data Based Error Correction

delete2020-01-01
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T
Taehyun Kwon *
M
Muhammad Imran
J
Joon-Sung Yang
DOI:10.1109/ACCESS.2020.2974013delete
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摘要

摘要

En 中文
The existing charge-based memories like DRAM and flash are reaching their scaling limits. Phase change memory (PCM) is one of the most promising emerging memory technologies due to its good scalability and low leakage power. Multi-level cell (MLC) operation in PCM, which stores two or more bits in a single cell, is necessary to achieve a high storage density. However, a reduced resistance range in multiple storage levels of MLC PCM, introduces a lot of soft errors because of the resistance drift phenomenon. The poor reliability of MLC PCM requires strong error correction codes (ECC) which could severely degrade the storage density and performance. In this paper, we propose a cost-effective reliable MLC PCM architecture for improving MLC PCM reliability, storage density and performance. The proposed architecture exploits the data-dependent nature of resistance drift problem to reduce the ECC overhead. A simple state mapping is used to generate virtual data, which is half of the actual data size. ECC parity bits are generated based on virtual data bits instead of actual message bits, thus resulting in a reduced number of cells for parity bits. This improves the reliability and storage density of MLC PCM. The performance is also improved by minimizing the ECC overhead. Simulation results show an improvement of about 10(4) times in reliability and 10.9 & x0025; in storage density compared to a conventional MLC PCM which uses a typical error correction scheme. Performance and energy efficiency are also improved up to 13.7 & x0025; and 10 & x0025;, respectively, by the proposed architecture.
Keyword:
Phase change materials
Resistance
Computer architecture
Reliability
Error correction codes
Microprocessors
Programming
Emerging memories
phase change memory (PCM)
MLC PCM
resistance drift
error correction code (ECC)
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IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

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samsung
学者数:
8.6K
论文数: 6.4K
被引数: 8
S
sungkyunkwan university (skku)
学者数:
3.7W
论文数: 3.6W
被引数: 49
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