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A Novel Mitigation Method for Noise-Induced Temperature Error in CPU Thermal Control
- A Novel Mitigation Method for Noise-Induced Temperature Error in CPU Thermal Control
- Lee, Juseung; Kim, Dohwan; Park, Kyung-Joon; Eun, Yongsoon
- DGIST Authors
- Park, Kyung-Joon; Eun, Yongsoon
- Issue Date
- IEEE Access, 8, 94000-94009
- Article Type
- Author Keywords
- Temperature measurement; Temperature control; Temperature sensors; Thermal noise; Real-time systems; Noise measurement; Zero-mean thermal sensor noise; noise-induced error; thermal control for real-time systems
- SYSTEMS; DESIGN
- It has been reported that in the thermal control of real-time computing systems, zero-mean thermal sensor noise can induce a significant steady-state error between the target and actual temperatures of a CPU. Unlike the usual case of zero-mean sensor noise resulting in zero-mean temperature fluctuations around the target value, this noise-induced temperature error manifests in the form of a bias, i.e., the mean of the error is not zero. Existing work has analyzed the main cause of this error and produced a solution, known as TCUB-VS. However, this existing solution has a few drawbacks: the transient response is sluggish, and the exact value of the noise standard deviation is necessary in the design stage. In this paper, we propose a novel method of avoiding noise-induced temperature error while overcoming the limitations of the existing work. The proposed method uses an estimated CPU temperature for the part of the controller that is sensitive to noise while using actual measurements for the other part of the controller. In this way, our proposed method eliminates noise-induced temperature error and overcomes the drawbacks of the existing work. To show the efficacy of our proposed method, theoretical results are obtained using a stochastic averaging approach, and experimental results are presented along with simulations.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
DSC Lab(Dynamic Systems and Control Laboratory)
Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
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