Dependability Assessment of NAND Flash-memory for Mission-critical Applications

It is a matter of fact that NAND flash memory devices are well established in consumer market. However, it is not true that the same architectures adopted in the consumer market are suitable for mission critical applications like space. In fact, USB flash drives, digital cameras, MP3 players are usually adopted to store "less significant" data which are not changing frequently (e.g., MP3s, pictures, etc.). Therefore, in spite of NAND flash's drawbacks, a modest complexity is usually needed in the logic of commercial flash drives. On the other hand, mission critical applications have different reliability requirements from commercial scenarios. Moreover, they are usually playing in a hostile environment (e.g., the space) which contributes to worsen all the issues. We aim at providing practical valuable guidelines, comparisons and tradeoffs among the huge number of dimensions of fault tolerant methodologies for NAND flash applied to critical environments. We hope that such guidelines will be useful for our ongoing research and for all the interested reader

Decoding Cyclic Codes up to a New Bound on the Minimum Distance

A new lower bound on the minimum distance of q-ary cyclic codes is proposed. This bound improves upon the Bose-Chaudhuri-Hocquenghem (BCH) bound and, for some codes, upon the Hartmann-Tzeng (HT) bound. Several Boston bounds are special cases of our bound. For some classes of codes the bound on the minimum distance is refined. Furthermore, a quadratic-time decoding algorithm up to this new bound is developed. The determination of the error locations is based on the Euclidean Algorithm and a modified Chien search. The error evaluation is done by solving a generalization of Forney's formula

Dependability Assessment of NAND Flash-memory for Mission-critical Applications

It is a matter of fact that NAND flash memory devices are well established in consumer market. However, it is not true that the same architectures adopted in the consumer market are suitable for mission critical applications like space. In fact, USB flash drives, digital cameras, MP3 players are usually adopted to store "less significant" data which are not changing frequently (e.g., MP3s, pictures, etc.). Therefore, in spite of NAND flash’s drawbacks, a modest complexity is usually needed in the logic of commercial flash drives. On the other hand, mission critical applications have different reliability requirements from commercial scenarios. Moreover, they are usually playing in a hostile environment (e.g., the space) which contributes to worsen all the issues. We aim at providing practical valuable guidelines, comparisons and tradeoffs among the huge number of dimensions of fault tolerant methodologies for NAND flash applied to critical environments. We hope that such guidelines will be useful for our ongoing research and for all the interested readers

Polynomials in Error Detection and Correction in Data Communication System

The chapter gives an overview of the various types of errors encountered in a communication system. It discusses the various error detection and error correction codes. The role of polynomials in error detection and error correction is discussed in detail with the architecture for practical implementation of the codes in a communication channel

Decoding Five Times Extended Reed Solomon Codes Using Syndromes

Recently a new family of five times extended Reed Solomon codes constructed over certain finite fields GF(2 zeta), where zeta >= 3 is an odd integer, was discovered. Until now only an erasure decoding algorithm for these codes was published. In this paper a new decoding algorithm is presented, which allows correcting up to two errors in a codeword from the five times extended Reed Solomon codes. The proposed decoding algorithm is based on syndrome usage

Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing

The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications. A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd

VHDL implementation of reed-solomon coding

Forward Error Correction technique depending on the properties of the system or on the application in which the error correcting is to be introduced. Error control coding techniques are based on the addition of redundancy to the information message according to a prescribed rule thereby providing data a higher bit rate. This redundancy is exploited by decoder at the receiver end to decide which message bit actually transmitted. Reed-Solomon codes are an important sub – class of non binary Bose-Chaudhuri-Hocquenghem (BCH) codes. In digital communication, Reed-Solomon (RS) codes refer to as a part of channel coding that had becoming very significant to better withstand the effects of various channel impairments such as noise, interference and fading. This signal processing technique is designed to improve communication performance and can be deliberate as medium for accomplishing desirable system trade-offs. Galois field arithmetic is used for encoding and decoding of Reed – Solomon codes. Galois field multipliers are used for encoding the information block. The encoder attaches parity symbols to the data using a predetermined algorithm before transmission. At the decoder, the syndrome of the received codeword is calculated. VHDL implementation creates a flexible, fast method and high degree of parallelism for implementing the Reed – Solomon codes. The purpose of this thesis is to evaluate the performance of RS coding system using M-ary modulation over Additive White Gaussian Noise AWGN channel and implementation of RS encoder in VHDL. Computer simulation tool and MATLAB will be used to create and run extensively the entire simulation model for performance evaluation and VHDL is used to implemented the design of RS encoder.

Decoding interleaved Reed-Solomon codes beyond their joint error-correcting capability

International audienceA new probabilistic decoding algorithm for low-rate interleaved Reed-Solomon (IRS) codes is presented. This approach increases the error correcting capability of IRS codes compared to other known approaches (e.g. joint decoding) with high probability. It is a generalization of well-known decoding approaches and its complexity is quadratic with the length of the code. Asymptotic parameters of the new approach are calculated and simulation results are shown to illustrate its performance. Moreover, an upper bound on the failure probability is derived

FPGA-based architectures for next generation communications networks

This engineering doctorate concerns the application of Field Programmable Gate Array (FPGA) technology to some of the challenges faced in the design of next generation communications networks. The growth and convergence of such networks has fuelled demand for higher bandwidth systems, and a requirement to support a diverse range of payloads across the network span. The research which follows focuses on the development of FPGA-based architectures for two important paradigms in contemporary networking - Forward Error Correction and Packet Classification. The work seeks to combine analysis of the underlying algorithms and mathematical techniques which drive these applications, with an informed approach to the design of efficient FPGA-based circuits