Resistive random-access memory (ReRAM) with the crossbar structure is one promising candidate to be used as a next generation non-volatile memory device. In a crossbar ReRAM, in which a memristor is positioned on each row-column intersection, the sneak-path problem is one of the main challenges for a reliable readout. The sneak-path problem can be solved with additional selection devices. When some selection devices fail short, the sneak-path problem re-occurs. The re-occurred sneak-path problem is addressed in this paper. The re-occurred sneak-path event can be described combinatorially and its adverse effect can be modeled as a parallel interference. Based on a simple pilot construction, we probabilistically characterize the inter-cell dependency of the re-occurred sneak-path events. Utilizing this dependency, we propose adaptive thresholding schemes for resistive memory readout using side information provided by pilot cells. This estimation theoretic approach effectively reduces the bit-error rate while maintaining low redundancy overhead and low complexity.

A common way to protect data stored in DRAM and related memory systems is through the use of an error-correcting code such as the extended Hamming code. Traditionally, these error-correcting codes provide equal protection guarantees to all messages. In this paper, we focus on unequal message protection (UMP), in which a subset of messages is deemed as special, and is afforded additional error-correction protection while maintaining the same number of redundancy bits as the baseline code. UMP is a powerful approach when the special messages are chosen based on the knowledge of data patterns in context. Our objective is to construct deterministic, algebraic codes with guaranteed UMP properties, derive their cardinality bounds using novel combinatorial techniques, and to demonstrate their efficacy for realistic memory benchmarks. We first introduce a UMP alternative to the single-bit parity-check code, and then we generalize to a broader UMP code family, including a UMP alternative to the extended Hamming code, offering full double-error correction protection to special messages. Our UMP constructions, applied to main memory in high-performance computing applications, could lead to significant system-level benefits such as less frequent checkpoints in supercomputers and decreased risk of catastrophic failure from erroneous special messages.

Permutation codes have recently garnered substantial research interest due to their potential in various applications, including cloud storage systems, genome resequencing, and flash memories. In this paper, we study the theoretical bounds and constructions of permutation codes in the generalized Cayley metric. The generalized Cayley metric captures the number of generalized transposition errors in a permutation and subsumes previously studied error types, including transpositions and translocations, without imposing restrictions on the lengths and positions of the translocated segments. Based on the so-called breakpoint analysis method proposed by Chee and Vu, we first present a coding framework that leads to order-optimal constructions, thus improving upon the existing constructions that are not order-optimal. We then use this framework to also develop an order-optimal coding scheme that is additionally explicit and systematic.

In crossbar resistive memory, in which a memristor is positioned on each row-column intersection, the sneak-path problem is one of the main challenges for reliable readout. The sneak-path event can be described combinatorially and its adverse effect can be modeled as a parallel interference. In this paper, based on a high-rate coding scheme, we characterize the inter-cell dependency of sneak-path events probabilistically. Utilizing this dependency, we propose adaptive thresholding schemes for resistive memory readout using side information provided by precoded bits. This estimation theoretic approach effectively reduces the bit-error rate while maintaining low redundancy overhead and low complexity.

Error correction and detection are the core components of all modern memory systems. Current computing memory systems use simple coding schemes to simultaneously meet the resiliency and latency requirements. In this paper, we review our recent results on context-aware coding for computing memories, an approach that explicitly takes into account various intrinsic side information for improved robustness to faults. We discuss both error correction and detection, codes' theoretical properties, and provide examples of how these solutions can be implemented in practice. We explicitly describe the special case of the error localization codes. We also discuss promising future directions and connections with classical information theoretic concepts.

Enabled by new storage mediums, Computation-in-Memory is a novel architecture that has shown great potential in reducing the burden of massive data processing by bypassing the communication and memory access bottleneck. Suggested by Cassuto and Crammer, allowing for ultra-fast Hamming distance computations to be performed in resistive memory with low-level conductance measurements has the potential to drastically speed up many modern machine learning algorithms. Meanwhile, Hamming distance Computation-in-Memory remains a challenging task as a result of the non-negligible device variability in practical resistive memory. In this paper, build upon the work of Cassuto and Crammer, we study memristor variability due to two distinct sources: resistance variation, and the non-deterministic write process. First, we introduce a technique for estimating the Hamming distance under resistance variation alone. Then, we propose error-detection and error-correction schemes to deal with non-ideal write process. We then combine these results to concurrently address both sources of memristor variabilities. In order to preserve the low latency property of Computation-in-Memory, all of our approaches rely on only a single vector-level conductance measurement. We use so-called inversion coding as a key ingredient in our solutions and we prove the optimality of this code given the restrictions on bit-accessible information. Finally, we demonstrate the efficacy of our approaches on the k-nearest neighbors classifier.

A common way to protect data stored in DRAM and related memory systems is through the use of a single-error-correcting/double-error-detecting (SECDED) code. Traditionally, these error-correcting codes provide equal protection guarantees to all messages. In a recent work, we demonstrated enhanced error correction capabilities for SECDED codes by taking into account contextual side-information about the data. This paper is concerned with a closely related scenario: unequal message protection (UMP), where a subset of special messages is afforded additional error-correction ability. UMP is relevant to computing systems where certain messages are critical and failures cannot be tolerated. We study practical UMP constructions where messages are guaranteed either one or two bit-error-correction. We provide upper and lower bounds on the number of special messages. We introduce an explicit and practical code construction based on BCH subcodes and demonstrate the efficacy of our technique on data from the AxBench and SPEC CPU2006 benchmark suites.

Permutation codes have recently garnered substantial research interest. In this paper, we study the permutation codes in the generalized Cayley metric. The generalized Cayley metric captures the number of generalized transposition errors in a permutation, and subsumes existing error types including transpositions and translocations without imposing restrictions on the lengths and positions of the translocated segments. Relying on the breakpoint analysis proposed by Chee and Vu, we construct a new class of permutation codes without interleaving. Our coding scheme, although it is non-constructive, has an order-optimal rate, and in certain circumstances, the rate is higher than that of existing codes based on interleaving.

Recent studies on noisy decoding for LDPC codes rely on the assumption that the noise in each component is independent and perpetual. This paper examines a noisy decoding model that generalizes this approach: the noise is due to multi-state channels, where the channel states are governed by queue-like processes. This model is inspired by errors in decoders that are due to the high levels of radiation. This is an important problem, as modern non-volatile memories (NVMs) must perform well in high-radiation environments if they are to be used for deep space applications. High levels of radiation have a significant impact on floating gate-based NVMs, such as flash, and therefore, require well-tuned, powerful error-correcting codes for reliable data storage along with the decoders capable of handling radiation-induced noisy components. We introduce a noisy LDPC decoding model subsuming certain previously studied models. This model is better suited to represent transient errors-in both variable nodes and check nodes-and allows for a more refined analysis compared with older, coarser models. We perform a density evolution-like theoretical evaluation, applicable to both regular and irregular codes, optimize the voting threshold for a Gallager B/E-decoder, and analyze the resulting evaluation. We also examine the finite block length case.

IoT devices need reliable hardware at low cost. It is challenging to efficiently cope with both hard and soft faults in embedded scratchpad memories. To address this problem, we propose a two-step approach: FaultLink and Software-Defined Error-Localizing Codes (SDELC). FaultLink avoids hard faults found during testing by generating a custom-tailored application binary image for each individual chip. During software deployment-time, FaultLink optimally packs small sections of program code and data into fault-free segments of the memory address space and generates a custom linker script for a lazy-linking procedure. During run-time, SDELC deals with unpredictable soft faults via novel and inexpensive Ultra-Lightweight Error-Localizing Codes (UL-ELCs). These require fewer parity bits than single-error-correcting Hamming codes. Yet our UL-ELCs are more powerful than basic single-error-detecting parity: they localize single-bit errors to a specific chunk of a codeword. SDELC then heuristically recovers from these localized errors using a small embedded C library that exploits observable side information (SI) about the application’s memory contents. SI can be in the form of redundant data (value locality), legal/illegal instructions, etc. Our combined FaultLink+SDELC approach improves min-VDD by up to 440 mV and correctly recovers from up to 90% (70%) of random single-bit soft faults in data (instructions) with just three parity bits per 32-bit word.

This paper studies problems in data reconstruction, an important area with numerous applications. In particular, we examine the reconstruction of binary and nonbinary sequences from synchronization (insertion/deletion-correcting) codes. These sequences have been corrupted by a fixed number of symbol insertions (larger than the minimum edit distance of the code), yielding a number of distinct traces to be used for reconstruction. We wish to know the minimum number of traces needed for exact reconstruction. This is a general version of a problem tackled by Levenshtein for uncoded sequences. We introduce an exact formula for the maximum number of common supersequences shared by sequences at a certain edit distance, yielding an upper bound on the number of distinct traces necessary to guarantee exact reconstruction. Without specific knowledge of the code words, this upper bound is tight. We apply our results to the famous single deletion/insertion-correcting Varshamov-Tenengolts (VT) codes and show that a significant number of VT code word pairs achieve the worst case number of outputs needed for exact reconstruction. We also consider extensions to other channels, such as adversarial deletion and insertion/deletion channels and probabilistic channels.

Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definition of absorbing sets, which we call general absorbing sets (GASs), and an important subclass of GASs, which we refer to as general absorbing sets of type two (GASTs). Additionally, we study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM), which succinctly captures key properties in a GAST. Based on these new concepts, we then develop a general code optimization framework, and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Our optimized codes enjoy over one order (resp., half of an order) of magnitude performance gain in the uncorrectable BER (UBER) relative to the unoptimized codes (resp. the codes optimized for symmetric channels).

We study the problem of perfectly reconstructing sequences from traces. The sequences are codewords from a deletion/insertion-correcting code and the traces are the result of corruption by a fixed number of symbol insertions (larger than the minimum edit distance of the code.) This is the general version of a problem tackled by Levenshtein for uncoded sequences. We introduce an exact formula for the maximum number of common supersequences shared by sequences at a certain edit distance, yielding a tight upper bound on the number of distinct traces necessary to guarantee exact reconstruction. We apply our results to the famous single deletion/insertion-correcting Varshamov-Tenengolts (VT) codes and show that a significant number of VT codeword pairs achieve the worst-case number of outputs needed for exact reconstruction.

This paper studies codes that correct bursts of deletions. Namely, a code will be called a b-burst-correcting code if it can correct a deletion of any b consecutive bits. While the lower bound on the redundancy of such codes was shown by Levenshtein to be asymptotically log(n) + b - 1, the redundancy of the best code construction by Cheng et al. is b(log(n/b + 1)). In this paper we close on this gap and provide codes with redundancy at most log(n) + (b - 1) log(log(n)) + b - log(b). We also extend the burst deletion model to two more cases: 1. a deletion burst of at most b consecutive bits and 2. a deletion burst of size at most b (not necessarily consecutive). We extend our code construction for the first case and study the second case for b = 3, 4. The equivalent models for insertions are also studied and are shown to be equivalent to correcting the corresponding burst of deletions.

File synchronization is a critical component of many modern data sharing applications. File synchronization is particularly challenging when different versions of a file that need to be synchronized differ in some number of edits. Several recent works have studied exact synchronization under edit errors. In this paper, we extend the available analytical toolbox of file synchronization by focusing on a previously unaddressed case of approximate synchronization wherein the reconstructed file need not be the same as the original file, but rather only be within some predetermined distortion. We study the case when a binary file undergoes symbol-level deletion errors with some small deletion rate (so that the total number of deletions is linear in file length). We derive a simple upper bound on the optimal rate of information that the transmitter (owner of the original file) needs to provide to the receiver (owner of the edited file) to allow the receiver to reconstruct the original file to within a predefined target distortion. We then create an approximate synchronization algorithm based on interactive communication between the transmitter and the receiver, and analyze the expected normalized communication bandwidth of our algorithm for various target distortion levels. Lastly, we implement our algorithm and provide experimental results on the approximate synchronization of a noisy image.

Developing efficient algorithms to synchronize between different versions of files is an important problem with numerous applications. We consider the interactive synchronization protocol introduced by Yazdi and Dolecek, based on an earlier synchronization algorithm by Venkataramanan et al. Unlike preceding synchronization algorithms, Yazdi and Dolecek's algorithm is specifically designed to handle a number of deletions linear in the length of the file. We extend this algorithm in three ways. First, we handle nonbinary files. Second, these files contain symbols chosen according to nonuniform distributions. Finally, the files are modified by both insertions and deletions. We take into consideration the collision entropy of the source and refine the matching graph developed by Yazdi and Dolecek by appropriately placing weights on the matching graph edges. We compare our protocol with the widely used synchronization software rsync, and with the synchronization protocol by Venkataramanan et al. In addition, we provide tradeoffs between the number of rounds of communication and the total amount of bandwidth required to synchronize the two files under various implementation choices of the baseline algorithm. Finally, we show the robustness of the protocol under imperfect knowledge of the properties of the edit channel, which is the expected scenario in practice.

Although the insertion/deletion problem has been studied for more than fifty years, many results still remain elusive. The goal of this work is to present three novel theorems with a combinatorial flavor that shed further light on the structure and nature of insertions/deletions. In particular, we give an exact result for the maximum number of common supersequences between two sequences, extending older work by Levenshtein. We then generalize this result for sequences that have different lengths. Finally, we compute the exact neighborhood size for the binary circular (alternating) string Cn= 0101 ... 01. In addition to furthering our understanding of the insertion/deletion channel, these theorems can be used as building blocks in other applications. One such application is developing improved lower bounds on the sizes of insertion/deletion-correcting codes.

Research works exploring coding for Flash memories typically seek to correct errors taking place during normal device operation. In this paper, we study the design of codes that protect Flash devices dealing with the unusual class of errors caused by exposure to large radiation dosages. Significant radiation exposure can take place, for example, when Flash is used as on-board memory in satellites and space probes. We introduce an error model that captures the effects of radiation exposure. Such errors are asymmetric, with the additional feature that the degree (and direction) of asymmetry depends on the stored sequence. We develop an appropriate distance and an upper bound on the sizes of codes which correct such errors. We introduce and analyze several simple code constructions.

Error-correcting codes are a critical need for modern flash memories. Such codes are typically designed under the assumption that the voltage threshold distributions in flash cells are Gaussian. This assumption, however, is not realistic. This is particularly the case late in the lifetime of flash devices. A recent work by Parnell et al. provides a parameterized model of MLC (2-bit cell) flash which accurately represents the voltage threshold distributions for an operating period up to 10 times longer than the device's specified lifetime. We analyze this model from an information-theoretic perspective and compute capacity for the resulting channel. We extrapolate the channel from an MLC to a TLC (3-bit cell) model and we characterize the resulting errors. We show that errors under the improved model are highly asymmetric. We introduce a code construction explicitly designed to exploit the asymmetric nature of these errors, and measure its improvement against existing codes at large P/E cycle counts.