55,113 research outputs found
Trajectory Codes for Flash Memory
Flash memory is well-known for its inherent asymmetry: the flash-cell charge
levels are easy to increase but are hard to decrease. In a general rewriting
model, the stored data changes its value with certain patterns. The patterns of
data updates are determined by the data structure and the application, and are
independent of the constraints imposed by the storage medium. Thus, an
appropriate coding scheme is needed so that the data changes can be updated and
stored efficiently under the storage-medium's constraints.
In this paper, we define the general rewriting problem using a graph model.
It extends many known rewriting models such as floating codes, WOM codes,
buffer codes, etc. We present a new rewriting scheme for flash memories, called
the trajectory code, for rewriting the stored data as many times as possible
without block erasures. We prove that the trajectory code is asymptotically
optimal in a wide range of scenarios.
We also present randomized rewriting codes optimized for expected performance
(given arbitrary rewriting sequences). Our rewriting codes are shown to be
asymptotically optimal.Comment: Submitted to IEEE Trans. on Inform. Theor
Rewriting Codes for Joint Information Storage in Flash Memories
Memories whose storage cells transit irreversibly between
states have been common since the start of the data storage
technology. In recent years, flash memories have become a very
important family of such memories. A flash memory cell has q
states—state 0.1.....q-1 - and can only transit from a lower
state to a higher state before the expensive erasure operation takes
place. We study rewriting codes that enable the data stored in a
group of cells to be rewritten by only shifting the cells to higher
states. Since the considered state transitions are irreversible, the
number of rewrites is bounded. Our objective is to maximize the
number of times the data can be rewritten. We focus on the joint
storage of data in flash memories, and study two rewriting codes
for two different scenarios. The first code, called floating code, is for
the joint storage of multiple variables, where every rewrite changes
one variable. The second code, called buffer code, is for remembering
the most recent data in a data stream. Many of the codes
presented here are either optimal or asymptotically optimal. We
also present bounds to the performance of general codes. The results
show that rewriting codes can integrate a flash memory’s
rewriting capabilities for different variables to a high degree
Asymmetric Error Correction and Flash-Memory Rewriting using Polar Codes
We propose efficient coding schemes for two communication settings: 1.
asymmetric channels, and 2. channels with an informed encoder. These settings
are important in non-volatile memories, as well as optical and broadcast
communication. The schemes are based on non-linear polar codes, and they build
on and improve recent work on these settings. In asymmetric channels, we tackle
the exponential storage requirement of previously known schemes, that resulted
from the use of large Boolean functions. We propose an improved scheme, that
achieves the capacity of asymmetric channels with polynomial computational
complexity and storage requirement.
The proposed non-linear scheme is then generalized to the setting of channel
coding with an informed encoder, using a multicoding technique. We consider
specific instances of the scheme for flash memories, that incorporate
error-correction capabilities together with rewriting. Since the considered
codes are non-linear, they eliminate the requirement of previously known
schemes (called polar write-once-memory codes) for shared randomness between
the encoder and the decoder. Finally, we mention that the multicoding scheme is
also useful for broadcast communication in Marton's region, improving upon
previous schemes for this setting.Comment: Submitted to IEEE Transactions on Information Theory. Partially
presented at ISIT 201
A Cerebellar-model Associative Memory as a Generalized Random-access Memory
A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers
Trade-offs between Instantaneous and Total Capacity in Multi-Cell Flash Memories
The limited endurance of flash memories is a major
design concern for enterprise storage systems. We propose a
method to increase it by using relative (as opposed to fixed)
cell levels and by representing the information with Write
Asymmetric Memory (WAM) codes. Overall, our new method
enables faster writes, improved reliability as well as improved
endurance by allowing multiple writes between block erasures.
We study the capacity of the new WAM codes with relative levels,
where the information is represented by multiset permutations
induced by the charge levels, and show that it achieves the
capacity of any other WAM codes with the same number of
writes. Specifically, we prove that it has the potential to double
the total capacity of the memory. Since capacity can be achieved
only with cells that have a large number of levels, we propose a
new architecture that consists of multi-cells - each an aggregation
of a number of floating gate transistors
When Do WOM Codes Improve the Erasure Factor in Flash Memories?
Flash memory is a write-once medium in which reprogramming cells requires
first erasing the block that contains them. The lifetime of the flash is a
function of the number of block erasures and can be as small as several
thousands. To reduce the number of block erasures, pages, which are the
smallest write unit, are rewritten out-of-place in the memory. A Write-once
memory (WOM) code is a coding scheme which enables to write multiple times to
the block before an erasure. However, these codes come with significant rate
loss. For example, the rate for writing twice (with the same rate) is at most
0.77.
In this paper, we study WOM codes and their tradeoff between rate loss and
reduction in the number of block erasures, when pages are written uniformly at
random. First, we introduce a new measure, called erasure factor, that reflects
both the number of block erasures and the amount of data that can be written on
each block. A key point in our analysis is that this tradeoff depends upon the
specific implementation of WOM codes in the memory. We consider two systems
that use WOM codes; a conventional scheme that was commonly used, and a new
recent design that preserves the overall storage capacity. While the first
system can improve the erasure factor only when the storage rate is at most
0.6442, we show that the second scheme always improves this figure of merit.Comment: to be presented at ISIT 201
The Relativistic Hopfield network: rigorous results
The relativistic Hopfield model constitutes a generalization of the standard
Hopfield model that is derived by the formal analogy between the
statistical-mechanic framework embedding neural networks and the Lagrangian
mechanics describing a fictitious single-particle motion in the space of the
tuneable parameters of the network itself. In this analogy the cost-function of
the Hopfield model plays as the standard kinetic-energy term and its related
Mattis overlap (naturally bounded by one) plays as the velocity. The
Hamiltonian of the relativisitc model, once Taylor-expanded, results in a
P-spin series with alternate signs: the attractive contributions enhance the
information-storage capabilities of the network, while the repulsive
contributions allow for an easier unlearning of spurious states, conferring
overall more robustness to the system as a whole. Here we do not deepen the
information processing skills of this generalized Hopfield network, rather we
focus on its statistical mechanical foundation. In particular, relying on
Guerra's interpolation techniques, we prove the existence of the infinite
volume limit for the model free-energy and we give its explicit expression in
terms of the Mattis overlaps. By extremizing the free energy over the latter we
get the generalized self-consistent equations for these overlaps, as well as a
picture of criticality that is further corroborated by a fluctuation analysis.
These findings are in full agreement with the available previous results.Comment: 11 pages, 1 figur
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