Efficient implementation of error correction codes in modular code

The article develops an efficient implementation of an algorithm for detecting and correcting multivalued residual errors with a fixed number of calculations of the syndrome, regardless of the set of moduli size. Criteria for uniqueness are given that can be met by selecting moduli from a set of primes to satisfy the desired error correction capability. An extended version of the algorithm with an increase in the number of syndromes depending on the number of information moduli is proposed. It is proposed to remove the restriction imposed on the size of redundant moduli. Identifying the location of the error and finding the error vector requires only look-up tables and does not require arithmetic operations. In order to minimize the excess space, an extended algorithm is also proposed in which the number of syndromes and look-up tables increases with the number of information moduli, but the locations of errors can still be identified without requiring iterative computations. By using the approximate method, we have reduced the computational complexity of the algorithm for calculating the syndrome from quadratic to linear-logarithmic, depending on the number of bits in the dynamic range.

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Efficient implementation of error correction codes in hash tables

Microelectronics Reliability ◽

10.1016/j.microrel.2013.08.004 ◽

2014 ◽

Vol 54 (1) ◽

pp. 338-340

Author(s):

P. Reviriego ◽

S. Pontarelli ◽

J.A. Maestro ◽

M. Ottavi

Keyword(s):

Error Correction ◽

Efficient Implementation ◽

Error Correction Codes ◽

Hash Tables

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Error Correction Capability of Random Network Error Correction Codes

2007 IEEE International Symposium on Information Theory ◽

10.1109/isit.2007.4557447 ◽

2007 ◽

Cited By ~ 14

Author(s):

Huseyin Balli ◽

Xijin Yan ◽

Zhen Zhang

Keyword(s):

Error Correction ◽

Random Network ◽

Error Correction Codes ◽

Error Correction Capability

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Bidirectional Limited-Magnitude Error Correction Codes for Flash Memories

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e96.a.1602 ◽

2013 ◽

Vol E96.A (7) ◽

pp. 1602-1608 ◽

Cited By ~ 1

Author(s):

Myeongwoon JEON ◽

Jungwoo LEE

Keyword(s):

Error Correction ◽

Error Correction Codes ◽

Flash Memories

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Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21062009 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2009

Author(s):

Fatemeh Najafi ◽

Masoud Kaveh ◽

Diego Martín ◽

Mohammad Reza Mosavi

Keyword(s):

Error Correction ◽

Random Access ◽

Error Correction Codes ◽

Access Memory ◽

Deep Convolutional Neural Networks ◽

Device Identification ◽

Physical Unclonable Functions ◽

Server Side ◽

Correction Technique ◽

Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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