Comparing reliabilities of centralized and distributed switching architectures for reconfigurable 2D arrays

<span>Whether used as main processing engines or as special-purpose adjuncts, processor arrays are capable of boosting performance for a variety of computation-intensive applications. For large processor arrays, needed to achieve the required performance level in the age of big data, processor malfunctions, resulting in loss of computational capabilities, form a primary concern. There is no shortage of alternative reconfiguration architectures and associated algorithms for building robust processor arrays. However, a commensurately extensive body of knowledge about the reliability modeling aspects of such arrays is lacking. We study differences between 2D arrays with centralized and distributed switching, pointing out the advantages of the latter in terms of reliability, regularity, modularity, and VLSI realizability. Notions of reliability inversion (modeling uncertainties that might lead us to choose a less-reliable system over one with higher reliability) and modelability (system property that makes the derivation of tight reliability bounds possible, thus making reliability inversion much less likely) follow as important byproducts of our study.</span>

Multi-Objective Redundancy Allocation for Multi-State System Design Under Epistemic Uncertainty of Component States

Multi-state is a typical characteristic of engineered systems. Most existing studies of redundancy allocation problems (RAPs) for multi-state system (MSS) design assume that the state probabilities of redundant components are precisely known. However, due to lack of knowledge and/or ambiguous judgements from engineers/experts, the epistemic uncertainty associated with component states cannot be completely avoided and it is befitting to be represented as belief quantities. In this paper, a multi-objective RAP is developed for MSS design under the belief function theory. To address the epistemic uncertainty propagation from components to system reliability evaluation, an evidential network (EN) model is introduced to evaluate the reliability bounds of an MSS. The resulting multi-objective design optimization problem is resolved via a modified non-dominated sorting genetic algorithm II (NSGA-II), in which a set of new Pareto dominance criteria is put forth to compare any pair of feasible solutions under the belief function theory. A numerical case along with a SCADA system design is exemplified to demonstrate the efficiency of the EN model and the modified NSGA-II. As observed in our study, the EN model can properly handle the uncertainty propagation and achieve narrower reliability bounds than that of the existing methods. More importantly, the original nested design optimization formulation can be simplified into a one-stage optimization model by the proposed method.