research-article

Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey

Authors:

Santiago Pagani,

P. D. Sai Manoj,

Jörg HenkelAuthors Info & Claims

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Volume 39, Issue 1

Pages 101 - 116

https://doi.org/10.1109/TCAD.2018.2878168

Published: 01 January 2020 Publication History

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 <italic>a-priori</italic> 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 <italic>a-priori</italic> 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.

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Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A Survey
1. Computer systems organization
2. Computing methodologies
  1. Machine learning

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cover image IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Volume 39, Issue 1

Jan. 2020

277 pages

ISSN:0278-0070

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0278-0070 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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