A Holistic Solution for Reliability of 3D Parallel Systems

Monolithic 3D technology is emerging as a promising solution that can bring massive opportunities, but the gains can be hindered due to the reliability issues exaggerated by high temperature. Conventional reliability solutions focus on one specific feature and assume that the other required features would be provided by different solutions. Hence, this assumption has resulted in solutions that are proposed in isolation of each other and fail to consider the overall compatibility and the implied overheads of multiple isolated solutions for one system. This article proposes a holistic reliability management engine, R2D3, for post-Moore’s M3D parallel systems that have low yield and high failure rate. The proposed engine, comprising a controller, reconfigurable crossbars, and detection circuitry, provides concurrent single-replay detection and diagnosis, fault-mitigating repair, and aging-aware lifetime management at runtime. This holistic view enables us to create a solution that is highly effective while achieving a low overhead. Our solution achieves 96% coverage of defect; reduces V th degradation by 53%, leading to a 78% performance improvement on average over 8 years for an eight-core system; and ultimately yields a 2.16× longer mean-time-to-failure (MTTF) while incurring an overhead of 7.4% in area, 6.5% in power, and an 8.2% decrease in frequency.

Modeling of Risk Measure Bonds Using the Beta Model

Duration and convexity are important measures in fixed-income portfolio management. In this paper, we analyze this measure of the bonds by applying the beta model. The general usefulness of the beta probability distribution enhances its applicability in a wide range of reliability analyses, especially in the theory and practice of reliability management. We estimate the beta density function of the duration/convexity. This estimate is based on two important and simple models of short rates, namely, Vasicek and CIR (Cox, Ingersoll, and Ross CIR). The models are described and then their sensitivity of the models with respect to changes in the parameters is studied. We generate the stochastic interest rate on the duration and convexity model. The main results show that the beta probability distribution can be applied to model each phase of the risk function. This distribution approved its effectiveness, simplicity and flexibility. In this paper, we are interested in providing a decision-making tool for the manager in order to minimize the portfolio risk. It is helpful to have a model that is reasonably simple and suitable to different maturity of bonds. Also, it is widely used by investors for choosing bond portfolio immunization through the investment strategy. The finding also shows that the probability of risk measured by the reliability function is to highlight the relationship between duration/convexity and different risk levels. With these new results, this paper offers several implications for investors and risk management purposes.

Reliability Analysis of Intelligent Manufacturing Systems Based on Improved FMEA Combined With Machine Learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears increasingly important. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Therefore, this paper devises a method based on improved FMEA model combined with machine learning for complex systems and applies it to the reliability management of intelligent manufacturing systems. The structured network of failure modes is constructed based on the knowledge graph for the intelligent manufacturing systems. The grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes, hereafter the clustering analysis is employed to extract the features of failure modes. The results show that the proposed method can more accurately reflect the coupling relationship between the failure modes compared with the conventional FMEA method. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Dynamic Reliability Management in Neuromorphic Computing

Neuromorphic computing systems execute machine learning tasks designed with spiking neural networks. These systems are embracing non-volatile memory to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate non-volatile memories cause aging of CMOS-based transistors in each neuron and synapse circuit in the hardware, drifting the transistor’s parameters from their nominal values. If these circuits are used continuously for too long, the parameter drifts cannot be reversed, resulting in permanent degradation of circuit performance over time, eventually leading to hardware faults. Aggressive device scaling increases power density and temperature, which further accelerates the aging, challenging the reliable operation of neuromorphic systems. Existing reliability-oriented techniques periodically de-stress all neuron and synapse circuits in the hardware at fixed intervals, assuming worst-case operating conditions, without actually tracking their aging at run-time. To de-stress these circuits, normal operation must be interrupted, which introduces latency in spike generation and propagation, impacting the inter-spike interval and hence, performance (e.g., accuracy). We observe that in contrast to long-term aging, which permanently damages the hardware, short-term aging in scaled CMOS transistors is mostly due to bias temperature instability. The latter is heavily workload-dependent and, more importantly, partially reversible. We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems by designing an intelligent run-time manager (NCRTM), which dynamically de-stresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target. NCRTM de-stresses these circuits only when it is absolutely necessary to do so, otherwise reducing the performance impact by scheduling de-stress operations off the critical path. We evaluate NCRTM with state-of-the-art machine learning workloads on a neuromorphic hardware. Our results demonstrate that NCRTM significantly improves the reliability of neuromorphic hardware, with marginal impact on performance.

Highway Reliability Management Model Based on Depth Recursion for Transportation Demand

An effective estimation method for the highway reliability management according to the Zhukov usage model based on the recursive test is put forward. This method makes use of the important sampling technique to ensure that under the conditions of the unbiased reliability estimation, the depth recursion is used to measure the difference between the operation profile and the distribution of the zero variance sampling, to correct the test profile by adjusting the transition probability between all the states through the heuristic iterative process. It has proved theoretically that the reliability of the estimation using the modified test profile test is unbiased estimate with the variance of 0. Finally, the heuristic iterative algorithm for the generation of the optimal test profile of the highway reliability estimation is given. The simulation results show that the method put forward in this paper can significantly reduce the variance of the estimate compared with the Newton algorithm, and can increase the speed of the recursive test while improving the estimation accuracy at the same time. The research done in this paper can effectively meet the requirements of the transportation industry in the tertiary industry.

Improving the process of “input control” at the enterprises of military-industrial complex as a way to reduce the cost of reclamation work

Problem statement. The predominance of effective innovation industries, where scientific developments are the main driving force of the economy is a distinguisher of the manufacturing process in a postindustrial society. Estimate for management efficiency of a high-tech enterprise in the conditions of active innovation activity becomes relevant.The purpose. The article consider military-industrial complex enterprises of the Russian Federation, which work in constantly changing conditions of resource, legislative or other restrictions. The manufacturing of small-scale products with a given quality and reliability with limited financial resources is one of the features of such enterprises. The article examine the methodology for analyzing the enterprise performance indicators that produces special-purpose products and works in accordance with the requirements [1] of manufacturing and usage of serial products.Results. The article considered the estimate options for complex performance indicator of the product quality and reliability management system, evaluating the effectiveness of the input control, [serial products] usage and manufacturing processes.Practical relevance. The proposed method allows identifying the maximum number of product defects, with a glance of the practical development of the technology for their creation and the manufacturing process organizational and technical features.