DeSyRe: On-demand system reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect-/fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints. (C) 2013 Elsevier B.V. All rights reserved

Energy-Efficient Fault-Tolerant Systems

This book describes the state-of-the-art in energy efficient, fault-tolerant embedded systems. It covers the entire product lifecycle of electronic systems design, analysis and testing and includes discussion of both circuit and system-level approaches. Readers will be enabled to meet the conflicting design objectives of energy efficiency and fault-tolerance for reliability, given the up-to-date techniques presented.</p

K-nearest neighbor based methodology for accurate diagnosis of diabetes mellitus

Diabetes is one of the leading causes of death, disability and economic loss throughout the world. Type 2 diabetes is more common (90-95% worldwide) type of diabetes. However, it can be prevented or delayed by taking the right care and interventions which indeed an early diagnosis. There has been much advancement in the field of various machine learning algorithms specifically for medical diagnosis. But due to partially complete medical data sets, accuracy often decreases, results in more number of misclassification that can lead t o harmful complications. An accurate prediction and diagnosis of a disease becomes a challenging research problem for many researchers. Therefore, aimed to improve the diagnosis accuracy we have proposed a new methodology, based on novel preprocessing techniques, and K-nearest neighbor classifier. The effectiveness of the proposed methodology is validated with the help of various quantitative metrics and a comparative analysis, with previously reported studies using the same UCI dataset focusing on pima-diabetes disease diagnosis. This is the first work of its kind, where 100% classification accuracy is achieved by feature reduction from eight to two that shows the out performance of the proposed methodology over existing methods

Energy-Efficient Fault-Tolerant Systems

XIV, 335 p. 132 illus., 51 illus. in color.onlin