Online Machine Learning-based Temperature Prediction for Thermal-aware NoC System

Chen, Kun-Chih Jimmy; Liao, Yuan-Hou

doi:10.1109/isocc47750.2019.9027723

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…For example, most modern Intel desktop and server CPU chips have per-core thermal sensors [33]. On the other hand, online thermal estimation or prediction methods can also be used [7], [34], [35].…”

Section: Notations Definition C = (X Y)mentioning

confidence: 99%

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Wen

Wang

Singh

et al. 2022

IEEE Trans. Comput.

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As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for "hot" tasks to be migrated to run on a "cool" core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this paper, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50% reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.

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Section: Notations Definition C = (X Y)mentioning

confidence: 99%

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Wen

Wang

Singh

et al. 2022

IEEE Trans. Comput.

View full text Add to dashboard Cite

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“…Even considering global monitoring, the sensors' noise is a topic of discussion [14]. To face the inherent errors of thermal models and improve DTM benefits, Chen et al [15] propose an adaptive thermal model to predict the temperature dynamically according to machine-learning algorithms. Another strategy to support thermal monitoring is a neural network-based online thermal estimation based on performance counters [16].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Thermal Management in Many-Core Systems Leveraged by Abstract Modeling

Silva

Weber

Martins

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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For years, transistor size reduction led to a linear decrease in power dissipation. This is the Dennard scaling law, which ended in 2000's technology nodes because the supply voltage no longer scales. The consequence is the increase of the power density in integrated circuits, leading to high temperatures, accelerating aging effects. Dynamic thermal management (DTM) is a technique adopted at runtime to act in the system using the components' current temperature to minimize hotspots and peak temperatures. This paper aims to present a DTM for NoC-based many-cores, using an abstract model, to estimate the temperature at the processing element level and task migration as the main actuation mechanism. Results show up to 12% of temperature reduction and almost 10 o C of peak temperature reduction, highlighting the approach's effectiveness, compared to the pattering and spiral mappings.

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Online Machine Learning-based Temperature Prediction for Thermal-aware NoC System

Cited by 2 publications

References 5 publications

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Dynamic Thermal Management in Many-Core Systems Leveraged by Abstract Modeling

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