A control-theoretic approach to dynamic voltage scheduling

  • Authors:

  • Ankush Varma;Brinda Ganesh;Mainak Sen;Suchismita Roy Choudhury;Lakshmi Srinivasan;Bruce Jacob

  • Affiliations:

  • University of Maryland at College Park, College Park, MD;University of Maryland at College Park, College Park, MD;University of Maryland at College Park, College Park, MD;University of Maryland at College Park, College Park, MD;University of Maryland at College Park, College Park, MD;University of Maryland at College Park, College Park, MD

  • Venue:
  • Proceedings of the 2003 international conference on Compilers, architecture and synthesis for embedded systems
  • Year:
  • 2003

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Abstract

The development of energy-conscious embedded and/or mobile systems exposes a trade-off between energy consumption and system performance. Recent microprocessors have incorporated dynamic voltage scaling as a tool that system software can use to explore this trade-off. Developing appropriate heuristics to control this feature is a non-trivial venture; as has been shown in the past, voltage-scaling heuristics that closely track perceived performance requirements do not save much energy, while those that save the most energy tend to do so at the expense of performance resulting in poor response time, for example. We note that the task of dynamically scaling processor speed and voltage to meet changing performance requirements resembles a classical control-systems problem, and so we apply a bit of control theory to the task in order to define a new voltage-scaling algorithm. We find that, using our nqPID (not quite PID) algorithm, one can improve upon the current best-of-class heuristic Pering's AVGN algorithm, based on Govil's AGED AVERAGES algorithm and Weiser's PAST algorithm in both energy consumption and performance. The study is execution-based, not-trace-based; the voltage-scaling heuristics were integrated into an embedded operating system running on a Motorola M-CORE processor model. The applications studied are all members of the MediaBench benchmark suite.