BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction

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BlockHammer : Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction

Ma, Ruixian (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Wu, Fei (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Lu, Zhonghai (author): KTH,Elektronik och inbyggda system

Zhong, Wenmin (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Wu, Quilin (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Wan, Jiguang (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Xie, Changsheng (author): Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

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Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China Elektronik och inbyggda system (creator_code:org_t)

Institute of Electrical and Electronics Engineers Inc. 2020
2020
English.
In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 0278-0070 .- 1937-4151. ; , s. 1-1

Related links:: https://ieeexplore.i...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

NAND flash-based storage devices have gained a lot of popularity in recent years. Unfortunately, flash blocks suffer from limited endurance. For guaranteeing flash reliability, flash manufactures also prescribe a specified number of Program and Erase (P/E) cycles to define the endurance of flash blocks within the same chip. To extend the service lifetime of a flash-based device, existing works also assume that flash blocks have the same endurance and take P/E based wear-leveling algorithms which evenly distribute P/E cycle across flash blocks in the controller. However, many studies indicate flash blocks exhibit a wide endurance difference due to the fabrication process. The endurance of flash blocks is limited by the weakest block. Thus, the traditional P/E-based block retirement mechanism makes flash blocks underutilized. To best excavate the endurance of all blocks and improve the reliability of flash devices, we present BlockHammer, a process variation aware proactive failure prediction scheme. BlockHammer takes process variation and blocks similarity into consideration, it consists of a block classifier and a block lifetime predictor. Using machine learning technology, we first establish a block classifier to classify flash blocks into different classes. Based on the classification results, we then establish the block lifetime prediction model for different classes. Flash blocks belonging to the same class is assigned the same model. To verify the effectiveness of BlockHammer, we collect block data from a real NAND flash-based testing platform by emulating the true application scenario of NAND flash. We compare the predicted value and the tested value, the experimental results show the proposed proactive failure scheme can achieve more than 92% accuracy for flash blocks. Therefore, the block failure point can be accurately predicted using BlockHammer in advance, which greatly enhance the reliability of NAND flash. IEEE

Subject headings

TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

Keyword

Endurance
Failure
Machine learning.
NAND flash
Prediction
Computer system recovery
Durability
Forecasting
Learning systems
Machine learning
Memory architecture
Reliability
Virtual storage
Application scenario
Classification results
Fabrication process
Flash-based devices
Lifetime prediction models
Machine learning technology
Wear-leveling algorithms
NAND circuits

Publication and Content Type

ref (subject category)
art (subject category)

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By the author/editor: Ma, Ruixian; Wu, Fei; Lu, Zhonghai; Zhong, Wenmin; Wu, Quilin; Wan, Jiguang; show more...; Xie, Changsheng; show less...

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Embedded Systems

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By the university: Royal Institute of Technology

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