Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey.
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| Title: | Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey. |
|---|---|
| Authors: | Pagani, Santiago1 (AUTHOR) santiago.pagani@arm.com, Manoj, P. D. Sai2 (AUTHOR) saimanoj.p.2013@ieee.org, Jantsch, Axel3 (AUTHOR) axel.jantsch@tuwien.ac.at, Henkel, Jorg1 (AUTHOR) henkel@kit.edu |
| Source: | IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems. Jan2020, Vol. 39 Issue 1, p101-116. 16p. |
| Subjects: | Machine learning, Power density, Reinforcement learning, Multicore processors, Temporal databases |
| Abstract: | Due to the high integration density and roadblock of voltage scaling, modern multicore processors experience higher power densities than previous technology scaling nodes. When unattended, this issue might lead to temperature hot spots, that in turn may cause nonuniform aging, accelerate chip failure, impair reliability, and reduce the performance of the system. This paper presents an overview of several research efforts that propose to use machine learning (ML) techniques for power and thermal management on single-core and multicore processors. Traditional power and thermal management techniques rely on a certain a-priori knowledge of the chip’s thermal model, as well as information of the workloads/applications to be executed (e.g., transient and average power consumption). Nevertheless, these a-priori information is not always available, and even if it is, it cannot reflect the spatial and temporal uncertainties and variations that come from the environment, the hardware, or from the workloads/applications. Contrarily, techniques based on ML can potentially adapt to varying system conditions and workloads, learning from past events in order to improve themselves as the environment changes, resulting in improved management decisions. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 143315579 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pagani%2C+Santiago%22">Pagani, Santiago</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> santiago.pagani@arm.com</i><br /><searchLink fieldCode="AR" term="%22Manoj%2C+P%2E+D%2E+Sai%22">Manoj, P. D. Sai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> saimanoj.p.2013@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Jantsch%2C+Axel%22">Jantsch, Axel</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> axel.jantsch@tuwien.ac.at</i><br /><searchLink fieldCode="AR" term="%22Henkel%2C+Jorg%22">Henkel, Jorg</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> henkel@kit.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Computer-Aided+Design+of+Integrated+Circuits+%26+Systems%22">IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems</searchLink>. Jan2020, Vol. 39 Issue 1, p101-116. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Power+density%22">Power density</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multicore+processors%22">Multicore processors</searchLink><br /><searchLink fieldCode="DE" term="%22Temporal+databases%22">Temporal databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Due to the high integration density and roadblock of voltage scaling, modern multicore processors experience higher power densities than previous technology scaling nodes. When unattended, this issue might lead to temperature hot spots, that in turn may cause nonuniform aging, accelerate chip failure, impair reliability, and reduce the performance of the system. This paper presents an overview of several research efforts that propose to use machine learning (ML) techniques for power and thermal management on single-core and multicore processors. Traditional power and thermal management techniques rely on a certain a-priori knowledge of the chip’s thermal model, as well as information of the workloads/applications to be executed (e.g., transient and average power consumption). Nevertheless, these a-priori information is not always available, and even if it is, it cannot reflect the spatial and temporal uncertainties and variations that come from the environment, the hardware, or from the workloads/applications. Contrarily, techniques based on ML can potentially adapt to varying system conditions and workloads, learning from past events in order to improve themselves as the environment changes, resulting in improved management decisions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TCAD.2018.2878168 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 101 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Power density Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Multicore processors Type: general – SubjectFull: Temporal databases Type: general Titles: – TitleFull: Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pagani, Santiago – PersonEntity: Name: NameFull: Manoj, P. D. Sai – PersonEntity: Name: NameFull: Jantsch, Axel – PersonEntity: Name: NameFull: Henkel, Jorg IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 02780070 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems Type: main |
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