DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs

A. Alper Goksoy, Anish Krishnakumar, Md Sahil Hassan, Allen J. Farcas, Ali Akoglu, Radu Marculescu, Umit Y. Ogras

Research output: Contribution to journalArticlepeer-review

Abstract

Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between application-specific integrated circuits (ASICs) and general-purpose processors. Traditional operating system (OS) schedulers can undermine the potential of DSSoCs since their execution times can be orders of magnitude larger than the execution time of the task itself. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the benefits of a fast (low-overhead) scheduler and a slow (sophisticated, high-performance but high-overhead) scheduler. Experiments with five real-world streaming applications show that DAS consistently outperforms both the fast and slow schedulers. For 40 different workloads, DAS achieves 1.29× speedup and 45% lower EDP than the sophisticated scheduler at low data rates and 1.28× speedup and 37% lower EDP than the fast scheduler when the workload intensifies.

Original languageEnglish (US)
Pages (from-to)51-54
Number of pages4
JournalIEEE Embedded Systems Letters
Volume14
Issue number1
DOIs
StatePublished - Mar 1 2022

Keywords

  • Domain-specific system-on-chip (DSSoC)
  • machine learning
  • policy switching
  • runtime classification
  • task scheduling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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