Download or read book Combined Source and Channel Coding for Non volatile Flash Memories kombinierte Quellen und Kanalcodierung F r Flash Speicher written by Jürgen Freudenberger and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Channel and Source Coding for Non Volatile Flash Memories written by Mohammed Rajab and published by Springer Nature. This book was released on 2020-01-02 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mohammed Rajab proposes different technologies like the error correction coding (ECC), sources coding and offset calibration that aim to improve the reliability of the NAND flash memory with low implementation costs for industrial application. The author examines different ECC schemes based on concatenated codes like generalized concatenated codes (GCC) which are applicable for NAND flash memories by using the hard and soft input decoding. Furthermore, different data compression schemes are examined in order to reduce the write amplification effect and also to improve the error correct capability of the ECC by combining both schemes.
Download or read book Channel Coding Methods for Non Volatile Memories written by Lara Dolecek and published by . This book was released on 2016-01-13 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Channel Coding Methods for Non-Volatile Memories is a digestible, yet comprehensive, introduction to the topic for any serious researcher involved in coding for memory systems
Download or read book Coding for Flash Memories written by Eitan Yaakobi and published by . This book was released on 2011 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flash memories are, by far, the most important type of non-volatile memory in use today. They are employed widely in mobile, embedded, and mass-storage applications, and the growth in this sector continues at a staggering pace. Moreover, since flash memories do not suffer from the mechanical limitations of magnetic disk drives, solid-state drives have the potential to upstage the magnetic recording industry in the foreseeable future. The research goal of this dissertation is the discovery of new coding theory methods that supports efficient design of flash memories. Flash memory is comprised of blocks of cells, wherein each cell can take on q>̲ 2 levels. While increasing the cell level is easy, reducing its level can be accomplished only by erasing an entire block. Such block erasures are not only time-consuming, but also degrade the memory lifetime. Our main contribution in this research is the design of rewriting codes that maximize the number of times that information can be written prior to incurring a block erasure. Examples of such coding schemes are flash/floating codes and buffer codes, introduced by Jiang and Bruck et al. in 2007, and WOM-codes that were presented by Rivest and Shamir almost three decades ago. The overall goal in these codes is to maximize the amount of information written to a fixed number of cells in a fixed number of writes. Furthermore, the design of error-correcting codes in flash memories is extensively studied. It is shown how to modify WOM-codes to support an error-correction capability. Motivated by the asymmetry of the error behavior of flash memories and the work by Cassuto et al., a coding scheme to correct asymmetric errors is presented. An extensive empirical database of errors was used to develop a comprehensive understanding of the error behavior as well as to design specific error-correcting codes for flash memories. This research on flash memories is expanded to other directions. Wear leveling techniques are widely used in flash memories in order to reduce and balance block erasures. It is shown that coding schemes to be used in these techniques can significantly reduce the number block erasures incurred during data movement. Also, the design of parallel cell programming algorithms is studied for the specific constraints and behavior of flash cells.
Download or read book Channel Coding Methods for Non volatile Memories written by Lara Dolecek and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Non-volatile memories (NVMs) have emerged as the primary replacement of hard-disk drives for a variety of storage applications, including personal electronics, mobile computing, intelligent vehicles, enterprise storage, data warehousing, and data-intensive computing systems. Channel coding schemes are a necessary tool for ensuring target reliability and performance of NVMs. However, due to operational asymmetries in NVMs, conventional coding approaches - commonly based on designing for the Hamming metric - no longer apply. Given the immediate need for practical solutions and the shortfalls of existing methods, the fast-growing discipline of coding for NVMs has resulted in several key innovations that not only answer the needs of modern storage systems but also directly contribute to the analytical toolbox of coding theory at large. This monograph discusses recent advances in coding for NVMs, covering topics such as error correction coding based on novel algebraic and graph-based methods, rank modulation, rewriting codes, and constrained coding. Our goal for this work is multifold: to illuminate the advantages - as well as challenges - associated with modern NVMs, to present a succinct overview of several exciting recent developments in coding for memories, and, by presenting numerous potential research directions, to inspire other researchers to contribute to this timely and thriving discipline."--Publisher.
Download or read book Coding Techniques for Error Correction and Rewriting in Flash Memories written by Shoeb Ahmed Mohammed and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Flash memories have become the main type of non-volatile memories. They are widely used in mobile, embedded and mass-storage devices. Flash memories store data in floating-gate cells, where the amount of charge stored in cells 0́3 called cell levels 0́3 is used to represent data. To reduce the level of any cell, a whole cell block (about 106 cells) must be erased together and then reprogrammed. This operation, called block erasure, is very costly and brings significant challenges to cell programming and rewriting of data. To address these challenges, rank modulation and rewriting codes have been proposed for reliably storing and modifying data. However, for these new schemes, many problems still remain open. In this work, we study error-correcting rank-modulation codes and rewriting codes for flash memories. For the rank modulation scheme, we study a family of one- error-correcting codes, and present efficient encoding and decoding algorithms. For rewriting, we study a family of linear write-once memory (WOM) codes, and present an effective algorithm for rewriting using the codes. We analyze the performance of our solutions for both schemes.
Download or read book Channel Coding for Flash Memories written by Jens Spinner and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Joint Source Channel Coding Using Arithmetic Codes written by Bi Dongsheng and published by Springer. This book was released on 2009-11-06 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem. The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes. Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion
Download or read book Coding Techniques to Extend the Lifetime of Flash Memories written by Yi Liu and published by . This book was released on 2020 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: NAND flash memory has become a widely used data storage technology. It uses rectangular arrays, or blocks of floating-gate transistors (commonly referred to as cells) to store information. The flash memory cells gradually wear out with repeated writing and erasing, referred to as program/erase (P/E) cycling, but the damage caused by P/E cycling is dependent on the programmed cell level. For example, in SLC flash memory, each cell has two different states, erased and programmed, represented by 1 and 0, respectively. Storing 1 in a cell causes less damage, or wear, than storing 0. More generally, in multilevel flash memories, the cell wear is an increasing function of the programmed cell level. The main research goal of this dissertation is to design new coding techniques that can extend the lifetime of flahs [flash] memories. The damage caused by programming the cell is usually modeled as a cost, and increasing the lifetime of flash memories can be converted to the problem of encoding information for use on channels with a cost constraint. This type of code is often referred to as a shaping code. Therefore in this dissertation we study rate-constrained shaping codes for noiseless costly channels. We systematically investigate the fundamental performance limits of fixed-to-variable length shaping codes from a rate and distribution perspective for a memoryless channel. Then, we study a recently proposed rate-1 direct shaping code and study its error propagation property. In addition, we consider shaping codes for finite-state noiseless costly channels. One observation from the above analysis is that an optimal shaping code for a memoryless channel generates a codeword sequence that approximates an i.i.d. process, and an optimal shaping code for a finite-state channel generates a codeword sequence that approximates a stationary Markov process. In this dissertation, we study the connection between shaping codes and distribution matching codes that map a sequence of i.i.d. source symbols into an output sequence that approximates an i.i.d. or a stationary Markov process. In the flash memory device, the bit error count (BEC) behavior varies significantly among pages. Therefore we propose a bad page detector, which predicts whether a page will become a "bad" page in the near future based on its current and previous BEC information. Two machine learning algorithms, based upon time-dependent neural network and long-short term memory architectures, are used to design the detector.
Download or read book Channel Codes written by William E. Ryan and published by Cambridge University Press. This book was released on 2024-08-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
