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BlockHammer :
BlockHammer : Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction
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- Ma, Ruixian (författare)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
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- Wu, Fei (författare)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
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- Lu, Zhonghai (författare)
- KTH,Elektronik och inbyggda system
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- Zhong, Wenmin (författare)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
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- Wu, Quilin (författare)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
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- Wan, Jiguang (författare)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
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- Xie, Changsheng (författare)
- 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
- Engelska.
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Ingår i: 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
- Relaterad länk:
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https://ieeexplore.i...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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