Energy-driven integrated hardware-software optimizations using SimplePower

  • Authors:

  • N. Vijaykrishnan;M. Kandemir;M. J. Irwin;H. S. Kim;W. Ye

  • Affiliations:

  • Microsystems Design Lab, The Pennsylvania State University, University Park, PAMicrosystems Design Lab, The Pennsylvania State University, University Park, PA;Microsystems Design Lab, The Pennsylvania State University, University Park, PAMicrosystems Design Lab, The Pennsylvania State University, University Park, PA;Microsystems Design Lab, The Pennsylvania State University, University Park, PAMicrosystems Design Lab, The Pennsylvania State University, University Park, PA;Microsystems Design Lab, The Pennsylvania State University, University Park, PAMicrosystems Design Lab, The Pennsylvania State University, University Park, PA;Microsystems Design Lab, The Pennsylvania State University, University Park, PAMicrosystems Design Lab, The Pennsylvania State University, University Park, PA

  • Venue:
  • Proceedings of the 27th annual international symposium on Computer architecture
  • Year:
  • 2000

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Abstract

With the emergence of a plethora of embedded and portable applications, energy dissipation has joined throughput, area, and accuracy/precision as a major design constraint. Thus, designers must be concerned with both optimizing and estimating the energy consumption of circuits, architectures, and software. Most of the research in energy optimization and/or estimation has focused on single components of the system and has not looked across the interacting spectrum of the hardware and software. The novelty of our new energy estimation framework, SimplePower, is that it evaluates the energy considering the system as a whole rather than just as a sum of parts, and that it concurrently supports both compiler and architectural experimentation.We present the design and use of the SimplePower framework that includes a transition-sensitive, cycle-accurate datapath energy model that interfaces with analytical and transition sensitive energy models for the memory and bus subsystems, respectively. We analyzed the energy consumption of ten codes from the multidimensional array domain, a domain that is important for embedded video and signal processing systems, after applying different compiler and architectural optimizations. Our experiments demonstrate that early estimates from the SimplePower energy estimation framework can help identify the system energy hotspots and enable architects and compiler designers to focus their efforts on these areas.