research-article

Dynamic thermal management for multimedia applications using machine learning

Authors:

Qinru QiuAuthors Info & Claims

DAC '11: Proceedings of the 48th Design Automation Conference

Pages 95 - 100

https://doi.org/10.1145/2024724.2024746

Published: 05 June 2011 Publication History

Abstract

Multimedia applications are expected to form the largest portion of workload in general purpose PC and portable devices. The ever-increasing computation intensity of multimedia applications elevates the processor temperature and consequently impairs the reliability and performance of the system. In this paper, we propose to perform dynamic thermal management using reinforcement learning algorithm for multimedia applications. The proposed learning model does not need any prior knowledge of the workload information or the system thermal and power characteristics. It learns the temperature change and workload switching patterns by observing the temperature sensor and event counters on the processor, and finds the management policy that provides good performance-thermal tradeoff during the runtime. We validated our model on a Dell personal computer with Intel Core 2 processor. Experimental results show that our approach provides considerable performance improvements with marginal increase in the percentage of thermal hotspot comparing to existing workload phase detection approach.

References

[1]

MediaBench: http://euler.slu.edu/~fritts/mediabench/

[2]

"Enhanced Intel SpeedStep® Technology - How To Document", http://www.intel.com/cd/channel/reseller/asmo-na/eng/203838.htm

[3]

AMDPowerNow:http://www.amd.com/us/products/technologies/power-management/Pages/power-management.aspx

[4]

Perfmon2: http://perfmon2.sourceforge.net/pfmon_usersguide.html

[5]

R. Cochran and S. Reda, "Consistent Runtime Thermal Prediction and Control Through Workload Phase Detection," In Proc. Design Automation Conference, June 2010.

Digital Library

[6]

A. Coskun, T. Rosing and K. Gross, "Utilizing predictors for efficient thermal management in multiprocessor SoCs," In IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 28, no. 10, pp 1503--1516, Oct. 2009.

Digital Library

[7]

R. Jayaseelan, T. Mitra, "Dynamic Thermal Management via Architectural Adaption", In Proc. Design Automation Conference, pp 484--489, Jul. 2009.

Digital Library

[8]

H. Jung, P. Rong and M. Pedram, "Stochastic Modeling of a Thermally-Managed Multi-Core System," In Proc. DAC, June 2008.

Digital Library

[9]

E. Pakbaznia, M. Ghasemazar, and M. Pedram,"Temperature Aware Dynamic Resource Provisioning in a Power Optimized Datacenter," In Proc. of Design Automation and Test in Europe, Mar. 2010.

Digital Library

[10]

K. Skadron, M. Stan, K. Sankaranarayanan, W. Huang, S. Velusamy and D. Tarjan, "Temperature-Aware Microarchitecture: Modeling and Implementation," ACM Trans. on Architecture and Code Optimization, Vol. 1 Issue 1, pages 94--125, Mar. 2004.

Digital Library

[11]

J. Srinivasan and S. Adve, "Predictive dynamic thermal management for multimedia applications," In Proc. ICS, 2003

Digital Library

[12]

R. Sutton and A. Barto, "Reinforcement Learning: An Introduction", MIT Press, Mar. 1998.

Digital Library

[13]

Y. Tan, W. Liu and Q. Qiu, "Adaptive Power Management Using Reinforcement Learning," In Proc. ICCAD, Nov. 2009.

Digital Library

[14]

P. Tan, M. Steinbach, V. Kumar, "Introduction to Data Mining", Addison-Wesley, May. 2005.

Digital Library

[15]

Y. Wang, K. Ma and X. Wang, "Temperature-constrained power control for chip multiprocessors with online model estimation," In Proc. ISCA, 2009, pp. 314--324.

Digital Library

[16]

I. Yeo, E. Kim, "Hybrid Dynamic Thermal Management Based on Statistical Characteristics of Multimedia Applications," In Proc. ISLPED, Aug. 2008.

Digital Library

[17]

S. Zhang and K. Chatha, "Thermal aware task sequencing on embedded processors," In Proc. DAC, June 2010.

Digital Library

Cited By

Nisha PVinay RLaad KSasmal PHaraki TJuyal CGabir Elbakri MAcharyya A(2024)Run-Time Prevention of Thermal Throttling on the Edge using Reinforcement-Learning Based Predictive Thermal Aware Power and Performance Management2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS)10.1109/NewCAS58973.2024.10666109(273-277)Online publication date: 16-Jun-2024
https://doi.org/10.1109/NewCAS58973.2024.10666109
Giardino MSchwyn DFerri BFerri A(2022)Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSMJournal of Low Power Electronics and Applications10.3390/jlpea1202002912:2(29)Online publication date: 19-May-2022
https://doi.org/10.3390/jlpea12020029
Niknia FHakami VRezaee K(2022)An SMDP-based approach to thermal-aware task scheduling in NoC-based MPSoC platformsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.03.016165(79-106)Online publication date: Jul-2022
https://doi.org/10.1016/j.jpdc.2022.03.016
Show More Cited By

Index Terms

Dynamic thermal management for multimedia applications using machine learning
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. General and reference
  1. Cross-computing tools and techniques
    1. Reliability

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Published In

cover image ACM Conferences

DAC '11: Proceedings of the 48th Design Automation Conference

June 2011

1055 pages

ISBN:9781450306362

DOI:10.1145/2024724

General Chair:
Leon Stok
IBM Corp., Hopewell Jct., NY
,
Program Chairs:
Nikil Dutt
Univ. of California, Irvine, CA
,
Soha Hassoun
Tufts Univ., Medford, MA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE Council on Electronic Design Automation (CEDA)

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2011

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Division of Computer and Network Systems

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DAC '11

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EDAC
SIGDA

DAC '11: The 48th Annual Design Automation Conference 2011

June 5 - 10, 2011

California, San Diego

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

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Cited By

Nisha PVinay RLaad KSasmal PHaraki TJuyal CGabir Elbakri MAcharyya A(2024)Run-Time Prevention of Thermal Throttling on the Edge using Reinforcement-Learning Based Predictive Thermal Aware Power and Performance Management2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS)10.1109/NewCAS58973.2024.10666109(273-277)Online publication date: 16-Jun-2024
https://doi.org/10.1109/NewCAS58973.2024.10666109
Giardino MSchwyn DFerri BFerri A(2022)Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSMJournal of Low Power Electronics and Applications10.3390/jlpea1202002912:2(29)Online publication date: 19-May-2022
https://doi.org/10.3390/jlpea12020029
Niknia FHakami VRezaee K(2022)An SMDP-based approach to thermal-aware task scheduling in NoC-based MPSoC platformsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.03.016165(79-106)Online publication date: Jul-2022
https://doi.org/10.1016/j.jpdc.2022.03.016
Giardino MSchwyn DFerri BFerri A(2021)2QoSM: A Q-Learner QoS Manager for Application-Guided Power-Aware Systems2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC51149.2021.00040(218-225)Online publication date: Dec-2021
https://doi.org/10.1109/MCSoC51149.2021.00040
Mandal SBhat GDoppa JPande POgras U(2020)An Energy-aware Online Learning Framework for Resource Management in Heterogeneous PlatformsACM Transactions on Design Automation of Electronic Systems10.1145/338635925:3(1-26)Online publication date: 13-May-2020
https://dl.acm.org/doi/10.1145/3386359
Li LGuo SMeng LZhai HHui ZMa BShen S(2020)An Improved Q-Learning for System Power Optimization with Temperature, Performance and Energy Constraint Modeling2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)10.1109/TOCS50858.2020.9339699(348-354)Online publication date: 11-Dec-2020
https://doi.org/10.1109/TOCS50858.2020.9339699
Pagani SManoj PJantsch AHenkel J(2020)Machine Learning for Power, Energy, and Thermal Management on Multicore Processors: A SurveyIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.287816839:1(101-116)Online publication date: Jan-2020
https://doi.org/10.1109/TCAD.2018.2878168
Niknia FRezaee KHakami V(2020)Adaptive Task Assignment for Thermal Management in Multi-Core Processing Systems2020 28th Iranian Conference on Electrical Engineering (ICEE)10.1109/ICEE50131.2020.9261036(1-7)Online publication date: 4-Aug-2020
https://doi.org/10.1109/ICEE50131.2020.9261036
Yang ZSerafy CLu TSrivastava A(2019)Enhanced Phase-Driven $Q$ -Learning-Based DRM for Multicore ProcessorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.287701438:11(2022-2031)Online publication date: Nov-2019
https://doi.org/10.1109/TCAD.2018.2877014
Gupta UMandal SMao MChakrabarti COgras U(2019)A Deep Q-Learning Approach for Dynamic Management of Heterogeneous ProcessorsIEEE Computer Architecture Letters10.1109/LCA.2019.289215118:1(14-17)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/LCA.2019.2892151
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