High-Efficient Reed-Solomon Decoder Based on Deep Learning

An, Xiangyu; Yu, Liang; Zhang, Wei

doi:10.1109/iscas45731.2020.9181187

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In (An et al, 2020), the Reed-Solomon neural decoder is introduced, which estimates the error of the received codewords, and adjust itself to do more accurate decoding. Neural Bose-Chaudhuri-Hocquenghem (BCH) codes decoding is introduced in (Kamassury & Silva, 2020;Nachmani & Wolf, 2019;Raviv et al, 2020).…”

Section: Error Correcting Codes With Deep Learningmentioning

confidence: 99%

Recovering AES Keys with a Deep Cold Boot Attack

Zimerman,

Nachmani,

Wolf

2021

Preprint

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Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. While most of the bits have been corrupted, many bits, at random locations, have not. Since the keys in many encryption schemes are being expanded in memory into longer keys with fixed redundancies, the keys can often be restored. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Even though AES consists of Rijndael S-box elements, that are specifically designed to be resistant to linear and differential cryptanalysis, our method provides a novel formalization of the AES key scheduling as a computational graph, which is implemented by a neural message passing network. Our results show that our methods outperform the state of the art attack methods by a very large margin.

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Section: Error Correcting Codes With Deep Learningmentioning

confidence: 99%

Recovering AES Keys with a Deep Cold Boot Attack

Zimerman,

Nachmani,

Wolf

2021

Preprint

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“…In recent years, new techniques have been emerged to improve the bit error rate performance of RS Codes over Additive White Gaussian Noise (AWGN) and Rayleigh channels such as multipath diversity approach [9]. Additionally, techniques based on deep learning models [10,11] as well as low complexity chase decoding [12,13] have been introduced to enhance the decoding capability of RS Codes.…”

Section: Introductionmentioning

confidence: 99%

Resilience of Reed-Solomon Codes against Single-Frequency Electromagnetic Disturbances: Fault Mechanisms and Fault Elimination through Symbol Inversion

Memar¹,

Vankeirsbilck²,

Vanoost³

et al. 2022

Electronics

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Modern safety-critical systems depend heavily on communication networks while operating in increasingly polluted electromagnetic environments. Forward Error Correction codes are increasingly being used in safety-critical applications; however, vulnerabilities can still be caused by undetected corrupted data. Within this paper, the effectiveness of primitive Reed–Solomon Codes under single-frequency electromagnetic disturbances is assessed. Additionally, the impact of various parameters including the message length, the Reed–Solomon Codes’ symbol size, and the amplitude of the induced voltages are also investigated. Simulations show that, at harmonics and some certain ratios of the bit-rate frequency, the susceptibility of Reed–Solomon Codes relative to this type of disturbance increases substantially. In worse-case scenarios, the rate of undetected corrupted data at these ratios could increase to values above 80%. It is shown that the main reason that Reed–Solomon Codes fail to detect such errors is due to the repetitive nature of code words’ symbols, as well as a special relation among the symbol size, the channel’s bit-rate, and the disturbance frequency. Accordingly, this paper proposes to add an extra inversion layer to the communication protocol to enhance the resiliency of these codes against single-frequency electromagnetic disturbances. Finally, it is shown that the proposed layer substantially mitigates the ratio of undetected corrupted data under the considered electromagnetic environment. By using the proposed approach, the rate of undetected corrupted data at the frequencies of concern decreased to values near 0%.

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GC-Like LDPC Code Construction and its NN-Aided Decoder Implementation

Hsu,

Liu,

Liao

et al. 2024

IEEE Open J. Circuits Syst.

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The trade-off between decoding performance and hardware costs has been a long-standing challenge in Low-Density Parity Check (LDPC) decoding. Based on model-driven methodology, the Neural Network-Aided Variable Weight Min-Sum (NN-aided vwMS) algorithm is proposed to address this dilemma in this paper. Not only eliminating the second minimum value in the check node update process for reducing hardware complexity, our approach featuring a fast-convergent shuffled scheduling method proposed to enhance convergence speed can also maintain similar decoding performance as compared to the traditional normalized min-sum algorithm. Different from existing model-driven methodologies only suitable for short codes, a Globally-Coupled Like (GC-like) LDPC code construction is presented to enable efficient training with simplified neural networks for longer LDPC codes. To demonstrate the capability of the NN-aided vwMS algorithm with the fast-convergent shuffled scheduling method, a GC-like (9126,8197) LDPC decoder is implemented for NAND flash applications, achieving a 6.56 Gbps throughput with a core area of 0.58 mm 2 under the 40-nm CMOS TSMC process, and average power consumption of 288 mW under the frame error rate of 2.64 × 10 −5 at 4.5dB. Compared to other works, our decoder architecture can accomplish a superior normalized throughput-to-area ratio of 11.31 Gbps/mm 2 , improving at least 2.4x on results in [1]-[4].

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High-Efficient Reed-Solomon Decoder Based on Deep Learning

Cited by 4 publications

References 14 publications

Recovering AES Keys with a Deep Cold Boot Attack

Recovering AES Keys with a Deep Cold Boot Attack

Resilience of Reed-Solomon Codes against Single-Frequency Electromagnetic Disturbances: Fault Mechanisms and Fault Elimination through Symbol Inversion

GC-Like LDPC Code Construction and its NN-Aided Decoder Implementation

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