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Abstract-How much margin do we have to add to the delay lines of a bundled-data circuit? This paper is an attempt to give a methodical answer to this question, taking into account all sources of variability and the existing EDA machinery for timing analysis and sign-off. The paper is based on the study of the margins of a ring oscillator that substitutes a PLL as clock generator. A timing model is proposed that shows that a 12% margin for delay lines can be sufficient to cover variability in a 65nm technology. In a typical scenario, performance and energy improvements between 15% and 35% can be obtained by using a ring oscillator instead of a PLL. The paper concludes that a synchronous circuit with a ring oscillator clock shows similar benefits in performance and energy as those of bundled-data asynchronous circuits.

Abstract-The growing variability in nanoelectronic devices, due to uncertainties from the manufacturing process and environmental conditions (power supply, temperature, aging), requires increasing design guardbands, forcing circuits to work with conservative clock frequencies. Various schemes for clock generation based on ring oscillators and adaptive clocks have been proposed with the goal to mitigate the power and performance losses attributable to variability. However, there has been no systematic analysis to quantify the benefits of such schemes and no signoff method has been proposed for timing correctness. This paper presents and analyzes a Reactive Clocking scheme with Variability-Tracking Jitter (RClk) that uses variability as an opportunity to reduce power by continuously adjusting the clock frequency to the varying environmental conditions, and thus, reduces guardband margins significantly. Power can be reduced between 20% and 40% at iso-performance and performance can be boosted by similar amounts at iso-power. Additionally, energy savings can be translated to substantial advantages in terms of reliability and thermal management. More importantly, the technology can be adopted with minimal modifications to conventional EDA flows.

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This article presents a comparative study of the performance of classification techniques used for fault diagnosis in industrial processes. The techniques studied ranging from classifiers based on Bayes theory as Maximum a Posteriori Probability (MAP) and Nearest Neighbor (kNN) classifiers, through minimizing an objective function such as Artificial Neural Networks (ANN) and Support Machines Vector (SVM) and ending with the parameter estimation technique Partial Least Squares (PLS). Comparison of these techniques is based on the capacity of classification of the historical data and the generalization of new observations. Also, a discussion about the robustness of the classifiers against the dimensionality reduction process is presented. The study was conducted using the data from the testing process "Tennessee Eastman Process" (TEP).