Coordinated Self-Tuning Thermal Management Controller for Mobile Devices

Chetoui, Sofiane; Reda, Sherief

doi:10.1109/mdat.2020.2977075

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…It has been shown that machine learning models for skin temperature estimation can be coupled with temperatureaware dynamic voltage and frequency scaling (DVFS) algorithms [26]. Similarly, a recent technique incorporates an online learning technique to model and manage the skin temperature of mobile SoCs [27]. Finally, skin temperature estimation can be enhanced using algorithms for sensor selection [28], that improves the placement of internal sensors.…”

Section: A Power and Thermal Modeling For Cpusmentioning

confidence: 99%

Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs

Mandal¹,

Ogras²,

Doppa³

et al. 2020

Preprint

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Dynamic resource management has become one of the major areas of research in modern computer and communication system design due to lower power consumption and higher performance demands. The number of integrated cores, level of heterogeneity and amount of control knobs increase steadily. As a result, the system complexity is increasing faster than our ability to optimize and dynamically manage the resources. Moreover, offline approaches are sub-optimal due to workload variations and large volume of new applications unknown at design time. This paper first reviews recent online learning techniques for predicting system performance, power, and temperature. Then, we describe the use of predictive models for online control using two modern approaches: imitation learning (IL) and an explicit nonlinear model predictive control (NMPC). Evaluations on a commercial mobile platform with 16 benchmarks show that the IL approach successfully adapts the control policy to unknown applications. The explicit NMPC provides 25% energy savings compared to a state-of-the-art algorithm for multi-variable power management of modern GPU sub-systems.

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Section: A Power and Thermal Modeling For Cpusmentioning

confidence: 99%