Self-Aware Thermal Management for High-Performance Computing Processors

Editor's note: Thermal management in high-performance multicore platforms has become exceedingly complex due to variable workloads, thermal heterogeneity, and long, thermal transients. This article addresses these complexities by sophisticated analysis of noisy thermal sensor readings, dynamic learning to adapt to the peculiarities of the hardware and the applications, and a dynamic optimization strategy. - Axel Jantsch, TU Wien - Nikil Dutt, University of California at Irvine

Noisy FIR identification as a quadratic eigenvalue problem

This paper describes a method for identifying FIR models in the presence of input and output noise. The proposed algorithm takes advantage of both the bias compensation principle and the instrumental variable method. It is based on a nonlinear system of equations whose unkowns are the FIR coefficients and the input noise variance. This system allows mapping the noisy FIR identification problem into a quadratic eigenvalue problem. The identification problem is thus solved without requiring the use of iterative least-squares algorithms. The performance of the proposed approach has been tested and compared with that of other identification methods by means of Monte Carlo simulations

Identificazione errors-in-variables e blind di sistemi dinamici

Dottorato di ricerca in ingegneria dei sistemi. 12. ciclo. Tutore Roberto Guidorzi. Coordinatore Giovanni MarroConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

A three-step identification procedure for ARARX models with additive measurement noise

This paper concerns the identification of extended ARARX models that consider also an additive white noise affecting the output. This model allows to take into account the presence of both a process disturbance and an additive measurement noise. A three-step identification procedure is described for identifying the extended ARARX model. The first step consists in an iterative bias-compensated least squares algorithm while the subsequent steps are based on simple (non-iterative) least squares equations. Simulation results are included to show the effectiveness of the proposed method

Fast filtering of noisy autoregressive signals

Autoregressive (AR) models are used in a wide variety of applications concerning the recovery of signals from noise-corrupted observations. In all real contexts of this kind also an additive broadband observation noise is present and the filtering of the observations is usually performed by means of standard Kalman filtering that requires a state space realization of the AR model to describe the observed process and the solution, at every step, of the Riccati equation. This paper proposes a faster filtering algorithm suitable for stationary processes and based on the decomposition of Toeplitz matrices described in (Rissanen, Mathematics of Computation, vol. 27, pp. 147-154, 1973) that operates directly on AR models. The computational complexity of the proposed algorithm increases only linearly with the order of the process

Structural health monitoring application of errors-in-variables identification

Structural Health Monitoring denotes a set of methodologies oriented to the description of the dynamical behavior of a structure in view of damage detection. These methodologies have taken advantage from the development of sensor, modeling and network techniques and constitute, today, a well established area. One of the most used methods consists in deducing dynamic models from the observations and in comparing these models with reference ones, concerning integrity conditions of the monitored structure. In many cases the excitations can be considered as White noise in the range of frequencies of interest and, in these cases, the structure can be described by means of autoregressive models. When this approximation is not realistic it is necessary to use input/output models that take into account also the characteristics of the excitation. This last case is considered in this paper making reference to the use of Errors\u2013in\u2013Variables (EIV) models and to data collected on a real structure during a small seismic event

On the use of minimal parametrizations in multivariable output-error identification

Multivariable output–error identification does not constitute, in any way, a straightforward extension of the scalar case. The aim of this paper is twofold: 1) Introduction of a new minimal parametrization for multivariable output error models leading to an easily implementable prediction error identification procedure; 2) Comparison of PEM and errors–in–variables approaches based on the dynamic Frisch scheme in the identification of MIMO output error processes

The Frisch scheme in multivariable errors-in-variables identification

This paper concerns the identification of multivariable errors-in-variables (EIV) models, i.e. models where all inputs and outputs are assumed as affected by additive errors. The identification of MIMO EIV models introduces challenges not present in SISO and MISO cases. The approach proposed in the paper is based on the extension of the dynamic Frisch scheme to the MIMO case. In particular, the described identification procedure relies on the association of EIV models with directions in the noise space and on the properties of a set of high order Yule\ue2\u80\u93Walker equations. A method for estimating the system structure is also described

Identification of errors-in-variables models with colored output noise

This paper deals with the problem of identifying linear errors-in-variables (EIV) models corrupted by white noise on the input and colored noise on the output. This allows to take into account the presence of both measurement errors and process disturbances. The proposed approach is based on a nonlinear system of equations whose unkowns are the system parameters and the input noise variance. The obtained set of equations allows mapping the EIV identification problem into a quadratic eigenvalue problem that, in turn, can be mapped into a linear generalized eigenvalue problem. The performance of the proposed approach is illustrated by means of Monte Carlo simulations and compared with those of existing techniques