3,442 research outputs found

    A 10-Gb/s two-dimensional eye-opening monitor in 0.13-μm standard CMOS

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    An eye-opening monitor (EOM) architecture that can capture a two-dimensional (2-D) map of the eye diagram of a high-speed data signal has been developed. Two single-quadrant phase rotators and one digital-to-analog converter (DAC) are used to generate rectangular masks with variable sizes and aspect ratios. Each mask is overlapped with the received eye diagram and the number of signal transitions inside the mask is recorded as error. The combination of rectangular masks with the same error creates error contours that overall provide a 2-D map of the eye. The authors have implemented a prototype circuit in 0.13-μm standard CMOS technology that operates up to 12.5 Gb/s at 1.2-V supply. The EOM maps the input eye to a 2-D error diagram with up to 68-dB mask error dynamic range. The left and right halves of the eyes are monitored separately to capture horizontally asymmetric eyes. The chip consumes 330 mW and operates reliably with supply voltages as low as 1 V at 10 Gb/s. The authors also present a detailed analysis that verifies if the measurements are in good agreement with the expected results

    The reliability of single-error protected computer memories

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    The lifetimes of computer memories which are protected with single-error-correcting-double-error-detecting (SEC-DED) codes are studies. The authors assume that there are five possible types of memory chip failure (single-cell, row, column, row-column and whole chip), and, after making a simplifying assumption (the Poisson assumption), have substantiated that experimentally. A simple closed-form expression is derived for the system reliability function. Using this formula and chip reliability data taken from published tables, it is possible to compute the mean time to failure for realistic memory systems

    An efficient graph representation for arithmetic circuit verification

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    Integrated Transversal Equalizers in High-Speed Fiber-Optic Systems

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    Intersymbol interference (ISI) caused by intermodal dispersion in multimode fibers is the major limiting factor in the achievable data rate or transmission distance in high-speed multimode fiber-optic links for local area networks applications. Compared with optical-domain and other electrical-domain dispersion compensation methods, equalization with transversal filters based on distributed circuit techniques presents a cost-effective and low-power solution. The design of integrated distributed transversal equalizers is described in detail with focus on delay lines and gain stages. This seven-tap distributed transversal equalizer prototype has been implemented in a commercial 0.18-µm SiGe BiCMOS process for 10-Gb/s multimode fiber-optic links. A seven-tap distributed transversal equalizer reduces the ISI of a 10-Gb/s signal after 800 m of 50-µm multimode fiber from 5 to 1.38 dB, and improves the bit-error rate from about 10^-5 to less than 10^-12

    Neural-network dedicated processor for solving competitive assignment problems

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    A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques

    CMOS circuit implementations for neuron models

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    The mathematical neuron basic cells used as basic cells in popular neural network architectures and algorithms are discussed. The most popular neuron models (without training) used in neural network architectures and algorithms (NNA) are considered, focusing on hardware implementation of neuron models used in NAA, and in emulation of biological systems. Mathematical descriptions and block diagram representations are utilized in an independent approach. Nonoscillatory and oscillatory models are discusse
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