Dynamic Power Management using adaptive learning tree

Eui Young Chung, Luca Benini, Giovanni De Micheli

Research output: Contribution to journalConference articlepeer-review

142 Citations (Scopus)


Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic systems by selectively shutting down idle components. The quality of the shutdown control algorithm (power management policy) mostly depends on the knowledge of user behavior, which in many cases is initially unknown or non-stationary. For this reason, DPM policies should be capable of adapting to changes in user behavior. In this paper, we present a novel DPM scheme based on idle period clustering and adaptive learning trees. We also provide a design guide for applying our technique to components with multiple sleep states. Experimental results show that our technique outperforms other advanced DPM schemes as well as simple time-out policies. The proposed approach shows little deviation of efficiency for various workloads having different characteristics, while other policies show that their efficiency changes drastically depending on the trace data characteristics. Furthermore, experimental evidence indicates that our workload learning algorithm is stable and has fast convergence.

Original languageEnglish
Pages (from-to)274-279
Number of pages6
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE/ACM International Conference on Computer-Aided Design (ICCAD-99) - San Jose, CA, USA
Duration: 1999 Nov 71999 Nov 11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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