The role of feedback in rewritable storage channels [Lecture Notes]

Franceschini, Michele; Lastras-Montao, L.A.; Mittelholzer, T.; Sharma, Mayank

doi:10.1109/msp.2009.934184

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…In [25], algorithms for parallel programming of flash memory cells were studied which were then extended in [20] as well as for the rank modulation scheme in [19]. Other works studied the programming schemes with continuousalphabet channels, see [7], [12], [13], [14], [15], [17], [18], [23] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Iterative Programming of Noisy Memory Cells

Horovitz

Yaakobi

Gad

et al. 2022

IEEE Trans. Commun.

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In this paper, we study a model, which was first presented by Bunte and Lapidoth, that mimics the programming operation of memory cells. Under this paradigm we assume that cells are programmed sequentially and individually. The programming process is modeled as transmission over a channel, while it is possible to read the cell state in order to determine its programming success, and in case of programming failure, to reprogram the cell again. Reprogramming a cell can reduce the bit error rate, however this comes with the price of increasing the overall programming time and thereby affecting the writing speed of the memory. An iterative programming scheme is an algorithm which specifies the number of attempts to program each cell. Given the programming channel and constraints on the average and maximum number of attempts to program a cell, we study programming schemes which maximize the number of bits that can be reliably stored in the memory. We extend the results by Bunte and Lapidoth and study this problem when the programming channel is either the BSC, BEC, or Z channel. For the BSC and the BEC our analysis is also extended for the case where the error probabilities on consecutive writes are not necessarily the same. Lastly, we also study a related model which is motivated by the synthesis process of DNA molecules.

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Section: Introductionmentioning

confidence: 99%

Iterative Programming of Noisy Memory Cells

Horovitz

Yaakobi

Gad

et al. 2022

IEEE Trans. Commun.

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“…The goal of the theory of rewritable channels [1][2] [3] is to provide the foundations for the study and design of techniques for the reliable storage of information on a medium that is impaired with write noise, resulting on a written value different from the one requested due to physical phenomena taking place at the time of writing. A direct motivation for this model is drawn from empirical observations that have been made on established and promising memory technologies such as FLASH [4] and Phase-Change-Memory (PCM) [5].…”

Section: Introductionmentioning

confidence: 99%

The capacity of the uniform noise rewritable channel with average cost

Lastras-Montaño

Franceschini

Mittelholzer

2010

2010 IEEE International Symposium on Information Theory

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We present a closed form expression for the capacity of the uniform noise rewritable channel with average write cost and a constraint on the input range. We show the existence of a critical cost κ 0 such that for all costs κ ≥ κ 0 , the capacity/cost tradeoff is given by an offset added to the logarithm of the cost. Assuming κ 0 > 1, for 1 ≤ κ < κ 0 the capacity/cost tradeoff grows faster than a logarithm.

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“…The theory of rewritable channels [1][2] [3][4] [5] studies the tradeoff between the information storage capacity of a medium and the cost for writing into it. One of the key elements of this theory is the concept of write noise in which a write attempt results in general in a written value different from the one intended.…”

Section: Introductionmentioning

confidence: 99%

Superposition coding in rewritable channels

Lastras-Montaño

Mittelholzer

Franceschini

2010

2010 Information Theory and Applications Workshop (ITA)

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Abstract-We introduce the notions of superposition coding and sequential decoding in the context of rewritable channels. Using these concepts we will show that for, where C(·) is the capacity of the rewritable channel for a given cost. A consequence of this result is that C(κ) ≥ C(1) + log κ, where C(1) is the classical channel capacity with no rewrite iterations. Thus this result provides a connection between rewritable and classical channel theory. We also derive a general upper bound on capacity which can be written as an offset plus the logarithm of the average number of write iterations. Closed form bounds on rewritable channel capacity will be given for Gaussian rewritable channels.

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The role of feedback in rewritable storage channels [Lecture Notes]

Cited by 7 publications

References 8 publications

Iterative Programming of Noisy Memory Cells

Iterative Programming of Noisy Memory Cells

The capacity of the uniform noise rewritable channel with average cost

Superposition coding in rewritable channels

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