A Comparative Study of Classification Methods for Flash Memory Error Rate Prediction

Fitzgerald, Barry; Fitzgerald, Jeannie; Ryan, Conor; Sullivan, Joseph A.

doi:10.1007/978-3-319-74690-6_38

Cited by 6 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gradient boosting was chosen as the machine learning method, as prior work by the authors showed it to be the most effective method on this type of data [5]. Gradient boosting is a machine learning technique that trains an initial weak model (usually based on decision trees), and then adds successive stages of models to minimise an error or loss function using a gradient descent-type procedure [8].…”

Section: A Machine Learning Methodsmentioning

confidence: 99%

See 1 more Smart Citation

An Early-Life NAND Flash Endurance Prediction System

Fitzgerald¹,

Ryan

Sullivan

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

NAND flash memory -ubiquitous in today's world of smart phones, SSDs (solid state drives), and cloud storage -has a number of well-known reliability problems. NAND data contains bit errors, which require the use of error correcting codes (ECCs). The raw bit error rate (RBER) increases with programerase (P-E) cycling, and the number of P-E cycles the device can withstand before the RBER exceeds the ECC capability is called its endurance. ECC operates on data stored in a sector of NAND, and there is a large variation in the endurance of sectors within a device and across devices, resulting in excessively conservative endurance specifications. This research shows, for the first time, that a sector's true endurance can be predicted with remarkable accuracy, using a combination of the sector's location within the device, and measurements taken at the very beginning of life. Real-world data is gathered on millions of NAND sectors using a custom-built test platform. Optimised machine learning classification models are built from the raw data to predict if a sector will pass or fail to a fixed ECC threshold, after a target P-E cycling level has been reached. A novel technique is demonstrated that uses different ECC thresholds for model training and testing, which allows the models to be tuned so that they never misclassify samples that would fail. This eliminates ECC failures and data loss, allowing simpler, less expensive ECC schemes to be used for modern NAND devices. It also enables significant endurance extensions to be achieved.

show abstract

Section: A Machine Learning Methodsmentioning

confidence: 99%

“…Previous works by the authors have developed prediction models based on data collected at the rated endurance, to determine how much further each sector could be cycled before exceeding an RBER threshold [3], [5], [6]. Sector errors, program time and erase time were all found to have predictive value for this purpose.…”

Section: Related Researchmentioning

confidence: 99%

An Early-Life NAND Flash Endurance Prediction System

Fitzgerald¹,

Ryan

Sullivan

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Barry Fitzgerald observed through a large amount of experimental data that the word line (WL) number, page type, and page parity in MLC flash memory will affect the code word error rate (CWER), the programming, and erasing duration [ 10 ]. Using the feature, the study proposed a sampling method based on the error probability density function [ 11 ], and constructed eight different two-class machine learning models. However, the study neglected the class balance of the data set.…”

Section: Introductionmentioning

confidence: 99%

An SVM-Based NAND Flash Endurance Prediction Method

Zhang

Wang²,

Zhuo

et al. 2021

Micromachines

View full text Add to dashboard Cite

NAND flash memory is widely used in communications, commercial servers, and cloud storage devices with a series of advantages such as high density, low cost, high speed, anti-magnetic, and anti-vibration. However, the reliability is increasingly getting worse while process improvements and technological advancements have brought higher storage densities to NAND flash memory. The degradation of reliability not only reduces the lifetime of the NAND flash memory but also causes the devices to be replaced prematurely based on the nominal value far below the minimum actual value, resulting in a great waste of lifetime. Using machine learning algorithms to accurately predict endurance levels can optimize wear-leveling strategies and warn bad memory blocks, which is of great significance for effectively extending the lifetime of NAND flash memory devices and avoiding serious losses caused by sudden failures. In this work, a multi-class endurance prediction scheme based on the SVM algorithm is proposed, which can predict the remaining P-E cycle level and the raw bit error level after various P-E cycles. Feature analysis based on endurance data is used to determine the basic elements of the model. Based on the error features, we present a variety of targeted optimization strategies, such as extracting the numerical features closely related to the endurance, and reducing the noise interference of transient faults through short-term repeated operations. Besides a high-parallel flash test platform supporting multiple protocols, a feature preprocessing module is constructed based on the ZYNQ-7030 chip. The pipelined module of SVM decision model can complete a single prediction within 37 us.

show abstract