Utilizing predictors for efficient thermal management in multiprocessor SoCs

  • Authors:
  • Ayse Kivilcim Coskun;Tajana Šimunic Rosing;Kenny C. Gross

  • Affiliations:
  • Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA;Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA;Sun Microsystems, San Diego, CA

  • Venue:
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Year:
  • 2009

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Abstract

Conventional thermal management techniques are reactive, as they take action after temperature reaches a threshold. Such approaches do not always minimize and balance the temperature, and they control temperature at a noticeable performance cost. This paper investigates how to use predictors for forecasting temperature and workload dynamics, and proposes proactive thermal management techniques for multiprocessor system-on-chips. The predictors we study include autoregressive moving average modeling and lookup tables. We evaluate several reactive and predictive techniques on an UltraSPARC T1 processor and an architecture-level simulator. Proactive methods achieve significantly better thermal profiles and performance in comparison to reactive policies.