Bias-Compensated Least Squares Identification of Distributed Thermal Models for Many-Core Systems-on-Chip

Diversi, Roberto; Tilli, Andrea; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca

doi:10.1109/tcsi.2014.2312495

Cited by 19 publications

(16 citation statements)

References 26 publications

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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%

“…This means that it is highly likely that the input is not highly exciting. In [21] some metrics (like the matrix condition number) have been proposed to evaluate the quality of the identified model, and in this work we test such assumptions. In the framework under study it is therefore necessary to include a block which performs the window selection procedure.…”

Section: B Thermal Model Of a Corementioning

confidence: 99%

See 2 more Smart Citations

Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data Selection

Pittino

Diversi

Benini

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise thermal control strategies as well as automated diagnosis. However, when dealing with large scale systems "in production", the accuracy of learned thermal models depends on the dynamics of the power excitation, which depends also on the executed workload, and measurement nonidealities, such as quantization. In this paper we show that, using an advanced system identification algorithm, we are able to generate very accurate thermal models (average error lower than our sensors quantization step of 1 • C) for a large scale HPC system on real workloads for very long time periods. However, we also show that: 1) not all real workloads allow for the identification of a good model; 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model. We then propose and validate a set of techniques based on machine learning and deep learning algorithms for the choice of data traces to be used for model identification. We also show that deep learning techniques are absolutely necessary to correctly choose such traces up to 96% of the times.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Thermal Model Of a Corementioning

confidence: 99%

See 1 more Smart Citation

Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data Selection

Pittino

Diversi

Benini

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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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%

Self-Aware Thermal Management for High-Performance Computing Processors

et al. 2018

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Processors for high performance computing and server workload are today thermally constrained. To preserve a safe working temperature, state-of-the-art processors for this market segment integrates many cores on the same die and feature fine-grain power management and thermal management feedback loops implemented in hardware. However, to keep the control policy simple, these controllers fail in taking advantage on the underlining thermal heterogeneity, long thermal transients and specific user mode. In this paper, we present a self-aware thermal management framework for making each chip self-aware of its thermal and workload peculiarities and use it to maximize performance and thermal sustainability.

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Thermal Characterization of a Tier0 Datacenter Room in Normal and Thermal Emergency Conditions

Ardebili

Cavazzoni

Benini

et al. 2021

Lecture Notes in Computer Science

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Bias-Compensated Least Squares Identification of Distributed Thermal Models for Many-Core Systems-on-Chip

Cited by 19 publications

References 26 publications

Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data Selection

Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data Selection

Self-Aware Thermal Management for High-Performance Computing Processors

Thermal Characterization of a Tier0 Datacenter Room in Normal and Thermal Emergency Conditions

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