Multilevel Redundancy Allocation for SEU Mitigation of DSP Codes Based on OMA

Yan, Yunyi; Hu, Changqing; Zhang, Lingxia; Ning, Weikang; Guo, Baolong

doi:10.1109/tdmr.2019.2899611

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Faults in ICs have been studied for over 40 years [5][6][7][8]. The most common appearance of these particles in space is the Single Event Upset (SEU) that cause unpredictable effects on running processes and electronic system outputs, such as data loss or error that may damage components, reduce performance, disrupt the data processing and even cause accidents [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

PCoSA: A product error correction code for use in memory devices targeting space applications

et al. 2020

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Section: Introductionmentioning

confidence: 99%

PCoSA: A product error correction code for use in memory devices targeting space applications

et al. 2020

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“…The RAP is an NP-hard problem whose computational demands exponentially grow with respect to the number of components in the system [19]. Consequently, various metaheuristic algorithms, such as variable neighborhood search (VNS) algorithms [45][46], genetic algorithms (GA) [47,51], simplified swarm optimization (SSO) algorithms [24,28], and a hybridization of SSO, particle swarm optimization (PSO) and simulated annealing (SA) [29], optimized memetics algorithm (OMA) [48][49], PSO [50], are adopted to resolve the RAP in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

Developing Model of Fuzzy Constraints Based on Redundancy Allocation Problem by an Improved Swarm Algorithm

2020

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This study aims to develop a novel credibility-based fuzzy model for the constraints of a series-parallel redundancy allocation problem (RAP) with a mix of components under an uncertainty environment and investigates the reliability enhancement of wireless sensor networks (WSN) through a RAP for objective system reliability by subjecting it to two nonlinear constraints, i.e., cost and weight constraints, under an uncertainty environment. In this work, fuzzy numbers have been adopted to overcome the problems related to the exact values of cost and weight of components used in system that are hard to obtain due to uncertainty in the real world. Next, the credibility theory is utilized to convert the fuzzy numbers to crisp numbers. In addition, the difficulty of the RAP encountered is the need to resiliently sustain the two nonlinear constraints, while also aiming to maximize system reliability by optimizing the redundancy allocation of components in parallel of each subsystem in the WSN. Hence, the model is solved by an improved simplified swarm optimization (ISSO). To prove the effectiveness of the ISSO in solving the researching model, experimental results are tested on random benchmarks. The experimental results show a significant efficiency and effectiveness of the proposed ISSO.

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Advanced DICE Based Triple-Node-Upset Recovery Latch with Optimized Overhead for Space Applications

Yan,

Li,

Gao

et al. 2023

2023 IEEE 32nd Asian Test Symposium (ATS)

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Multilevel Redundancy Allocation for SEU Mitigation of DSP Codes Based on OMA

Cited by 4 publications

References 24 publications

PCoSA: A product error correction code for use in memory devices targeting space applications

PCoSA: A product error correction code for use in memory devices targeting space applications

Developing Model of Fuzzy Constraints Based on Redundancy Allocation Problem by an Improved Swarm Algorithm

Advanced DICE Based Triple-Node-Upset Recovery Latch with Optimized Overhead for Space Applications

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