Programming Analog Computational Memory Elements to 0.2% Accuracy Over 3.5 Decades Using a Predictive Method

Bandyopadhyay, Abhishek; Serrano, G.; Hasler, P.

doi:10.1109/iscas.2005.1465045

Cited by 28 publications

(44 citation statements)

References 6 publications

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“…Suppose a permutation f ∈ S n was stored by setting the charge levels of n flash memory cells to c 1 …”

Section: Definitions and Notationsmentioning

confidence: 99%

“…Firstly, writing is more time-and energy-consuming than reading [4]. The main reason behind this asymmetry is the iterative cellprogramming procedure designed to avoid over-programming [1] (raising the cell's charge level above its target level). Cells can be programmed individually, however when erasing is needed, the flash memory can erase only entire blocks (today, containing approximately 10 5 cells, see [4]).…”

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confidence: 99%

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On the Labeling Problem of Permutation Group Codes Under the Infinity Metric

Tamo

Schwartz

2012

IEEE Trans. Inform. Theory

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Abstract-Codes over permutations under the infinity norm have been recently suggested as a coding scheme for correcting limited-magnitude errors in the rank modulation scheme. Given such a code, we show that a simple relabeling operation, which produces an isomorphic code, may drastically change the minimal distance of the code. Thus, we may choose a code structure for efficient encoding/decoding procedures, and then optimize the code's minimal distance via relabeling.We formally define the relabeling problem, and show that all codes may be relabeled to get a minimal distance at most 2. The decision problem of whether a code may be relabeled to distance 1 is shown to be NP-complete, and calculating the best achievable minimal distance after relabeling is proved hard to approximate.Finally, we consider general bounds on the relabeling problem. We specifically show the optimal relabeling distance of cyclic groups. A specific case of a general probabilistic argument is used to show AGL(p) may be relabeled to a minimal distance of p − O( p ln p).

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“…Suppose a permutation f ∈ S n was stored by setting the charge levels of n flash memory cells to c 1 …”

Section: Definitions and Notationsmentioning

confidence: 99%

mentioning

confidence: 99%

On the Labeling Problem of Permutation Group Codes Under the Infinity Metric

Tamo

Schwartz

2012

IEEE Trans. Inform. Theory

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“…Let z ∈ [x, y) be the number satisfying the following three properties: (1) With the algorithm S, z is one of the possible values of the cell level after a rounds, for some a ∈ {0, 1…”

Section: I) Is Continuous and Non-decreasing In X;mentioning

confidence: 99%

“…is programmed using a conservative approach: the memory circuit iteratively injects charge into the cell and then measures the level [1,3,4]. Initially, the cell is at level 0, which is the minimum value of the cell level.…”

Section: Introductionmentioning

confidence: 99%

On the capacity of flash memories

Jiang

Bruck

2008

2008 International Symposium on Information Theory and Its Applications

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Flash memories are the most widely used type of nonvolatile electronic memories. Compared to magnetic recording and optical recording, flash memories have the unique property that their cell levels, which represent data, are programmed using an iterative procedure that monotonically shifts each cell level upward toward its target value. In this paper, we study the capacity of flash memories to store data. We explore the relationship among their capacity, programming precision and programming time. The study is focused on the capacity of single cells, and an optimal programming algorithm is presented.

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“…Being the paramount challenge of Multilevel Flash memory implementation, fast accurate program schemes are a topic of significant research and design efforts [8], [2], [6]. All example above is one instantiation of a general construction method that provides codes for all possible code parameters.…”

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confidence: 99%

Codes for Multi-Level Flash Memories: Correcting Asymmetric Limited-Magnitude Errors

Cassuto

Schwartz

Bohossian

et al. 2007

2007 IEEE International Symposium on Information Theory

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Abstract-Several physical effects that limit the reliability and performance of Multilevel Flash memories induce errors that have low magnitude and are dominantly asymmetric. This paper studies block codes for asymmetric limited-magnitude errors over q-ary channels. We propose code constructions for such channels when the number of errors is bounded by t. The construction uses known codes for symmetric errors over small alphabets to protect large-alphabet symbols from asymmetric limited-magnitude errors. The encoding and decoding of these codes are performed over the small alphabet whose size depends only on the maximum error magnitude and is independent of the alphabet size of the outer code. An extension of the construction is proposed to include systematic codes as a benefit to practical implementation.

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Programming Analog Computational Memory Elements to 0.2% Accuracy Over 3.5 Decades Using a Predictive Method

Cited by 28 publications

References 6 publications

On the Labeling Problem of Permutation Group Codes Under the Infinity Metric

On the Labeling Problem of Permutation Group Codes Under the Infinity Metric

On the capacity of flash memories

Codes for Multi-Level Flash Memories: Correcting Asymmetric Limited-Magnitude Errors

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