Improving Flash Wear-Leveling by Proactively Moving Static Data

Chang, Yuan-Hao; Hsieh, Jen-Wei; Kuo, Tei-Wei

doi:10.1109/tc.2009.134

Cited by 104 publications

(38 citation statements)

References 12 publications

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“…Algorithm 1 shows a procedure of RRWL. In Algorithm 1, the triggering condition of wear leveling is same as the BET technique [2]. If wear leveling is triggered in line 2, a victim block is selected by k, BET, and RR index in line 10∼14.…”

Section: Round Robin-based Wear Levelingmentioning

confidence: 99%

“…In our experiments, we compare the performance of RRWL with BET and HaWL [2], [3]. Table 1 shows details of experiment parameters used.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Lifetime prolongation is the most important performance indicator in wear leveling. The lifetime of flash memory is based on the first time to occur any bad blocks [2]. In all cases of Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…However, these information tables require additional memory space and they have to be minimized in limited memory space requirements. Therefore, to reduce the memory consumption of information tables, block erase table (BET) techniques have been studied [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…BET is composed of a bit array and saves the histories of erase operations in each block [2]. Subsequently, during a given time period, if there are no erased blocks, BET techniques set the unerased blocks to cold blocks and migrates the cold blocks for wear leveling.…”

Section: Introductionmentioning

confidence: 99%

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RRWL: Round Robin-Based Wear Leveling Using Block Erase Table for Flash Memory

Kim

Choi

Kwak

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we propose a round robin-based wear leveling (RRWL) for flash memory systems. RRWL uses a block erase table (BET), which is composed of a bit array and saves the erasure histories of blocks. BET can use one-to-one mode to increase the performance of wear leveling or one-to-many mode to reduce memory consumption. However, one-to-many mode decreases the accuracy of cold block information, which results in the lifetime degradation of flash memory. To solve this problem, RRWL consistently uses one-to-one mode based on round robin method to increase the accuracy of cold block identification, with reduced memory size of BET, like in one-to-many mode. Experiments show that RRWL increases the lifetime of flash memory by up to 47% and 14%, compared with BET and HaWL, respectively. key words: flash memory, wear leveling, hidden cold block problem, bit array table, block erase table

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Section: Round Robin-based Wear Levelingmentioning

confidence: 99%

“…In our experiments, we compare the performance of RRWL with BET and HaWL [2], [3]. Table 1 shows details of experiment parameters used.…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RRWL: Round Robin-Based Wear Leveling Using Block Erase Table for Flash Memory

Kim

Choi

Kwak

2017

IEICE Trans. Inf. & Syst.

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Novel battery wear leveling method for large‐scale reconfigurable battery packs

et al. 2020

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Summary As the market and the application areas of high capacity battery energy storage systems are rapidly increasing, there is a correspondingly high interest in the topic of minimizing battery state of health degradation in battery packs. In this article, a novel method for battery management in large‐scale battery packs is introduced, aiming to minimize battery degradation by enforcing a special wear leveling (WL) policy, adapted from the flash memory arrays. Using this method in conjunction with a hybrid mathematical‐electrochemical battery model, a reconfigurable battery management system (BMS) is proposed and evaluated. The results of the performance analysis and in‐depth comparisons with other state‐of‐the‐art solution shows that the proposed method achieves significantly longer operating times for the battery packs—for example, 415% improvement over the classical BMS in the load current variation scenario. As the computing and memory requirements are relatively low, the new battery WL method can also be implemented on embedded systems with limited resources.

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Wear Minimization for Cuckoo Hashing: How Not to Throw a Lot of Eggs into One Basket

Eppstein

Goodrich

Mitzenmacher

et al. 2014

Experimental Algorithms

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We study wear-leveling techniques for cuckoo hashing, showing that it is possible to achieve a memory wear bound of log log n + O(1) after the insertion of n items into a table of size Cn for a suitable constant C using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically, showing that it significantly improves on the memory wear performance for classic cuckoo hashing and linear probing in practice.

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Improving Flash Wear-Leveling by Proactively Moving Static Data

Cited by 104 publications

References 12 publications

RRWL: Round Robin-Based Wear Leveling Using Block Erase Table for Flash Memory

RRWL: Round Robin-Based Wear Leveling Using Block Erase Table for Flash Memory

Novel battery wear leveling method for large‐scale reconfigurable battery packs

Wear Minimization for Cuckoo Hashing: How Not to Throw a Lot of Eggs into One Basket

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