2014
DOI: 10.1109/tcsi.2014.2312495
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Bias-Compensated Least Squares Identification of Distributed Thermal Models for Many-Core Systems-on-Chip

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Cited by 20 publications
(16 citation statements)
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“…Several strategies have been proposed in the last decade for extracting compact thermal models directly from a processor chip's thermal response to a given power/workload stress input [16], [17], [18], [19], [8], [20], [21], [22], [23], [10]. The simplest ones do not account for the multimodal nature of the thermal transient caused by the different building materials and their relative time-constants (i.e.…”
Section: Related Workmentioning
confidence: 99%
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“…Several strategies have been proposed in the last decade for extracting compact thermal models directly from a processor chip's thermal response to a given power/workload stress input [16], [17], [18], [19], [8], [20], [21], [22], [23], [10]. The simplest ones do not account for the multimodal nature of the thermal transient caused by the different building materials and their relative time-constants (i.e.…”
Section: Related Workmentioning
confidence: 99%
“…To take into account the presence of this noise, MISO ARX models with noisy input and output have been considered [22], [21]. These models belong to the family of errors-in-variables models and cannot be identified by means of standard least squares and prediction error methods [25].…”
Section: Related Workmentioning
confidence: 99%
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“…is a white process with variance and Previous works have shown that the relation between the core temperature and the dissipated power can be described by a purely dynamic model [9].…”
Section: The Self-learning Policymentioning
confidence: 99%
“…Two important features of these models are the possibility of obtaining asymptotically unbiased estimates of their parameters by means of least squares and the absence of stability problems of the associated optimal one step-ahead predictors [10]. Nevertheless, it has been shown in [9] and [11] that the classic MISO ARX model (1.1) is not able to describe properly the thermal dynamics of the system because the estimated models are characterized by relevant negative poles and/or complex conjugate poles. This is in contrast with the physics of thermal systems, where only real positive poles can exist.…”
Section: The Self-learning Policymentioning
confidence: 99%