1 / 13

Architecture-Level Power Modeling

Architecture-Level Power Modeling. N. Kim, T. Austin, T. Mudge, and D. Grunwald. “Challenges for Architectural Level Power Modeling.” In Power Aware Computing , (R. Melhem and R. Graybill eds.), Kluwer, 2001. Kevin Skadron Mircea Stan. Who Cares?.

bly
Download Presentation

Architecture-Level Power Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Architecture-LevelPower Modeling N. Kim, T. Austin, T. Mudge, and D. Grunwald. “Challenges for Architectural Level Power Modeling.” In Power Aware Computing, (R. Melhem and R. Graybill eds.), Kluwer, 2001. Kevin Skadron Mircea Stan

  2. Who Cares? • Power is now a first-level design constraint for both embedded/mobile and high-performance/general-purpose processors • Battery life (eg, laptops) • Heat removal and package cost • Degradation and lifetime Architects don’t have good tools to model this

  3. Modeling • What architects normally do: model behavior/performance at the cycle level (eg, SimpleScalar) • Many abstractions and simplifications • Examples: I-cache, memory buses • Faster than a more detailed model; still good enough • Power and heat, however, require more implementation detail • Current power-performance simulators try to omit the extra detail by using abstractions or analytic models

  4. Current Arch.-Level Power Simulators • Wattch (Brooks et al.) • Doesn’t model anything outside the core (eg, external bus) • CACTI-based models for large structures (cache, branch predictor, register file, instruction window, etc.) • Tuned using Intel data • SimplePower (Vijaykrishnan et al.) • Adds bus and memory modeling, also I/O pads • Look-up-tables (LUTs) • Tempest (Cai & Lim) • Power density • Chip-level thermal modeling • No one: data sensitivity, clock tree, global interconnect

  5. Typical Power-Performance Modeling Technologyparameters Micro-arch.config Staticpower model Cycle-accurateperformancemodel Dynamicpowerestimation cycle-by-cyclestatistics activityfactors

  6. Power Basics P = ½ACV2f + AVIshort + VIleak • A = activity factor • C = capacitance • V = dynamic voltage • f = frequency • Ishort = short-circuit current during switching • Ileak = leakage current

  7. Power Basics P = ½ACV2f + AVIshort + VIleakP = ACVDDVswingf + AVIshort + VIleak • A = activity factor • C = capacitance • V = dynamic voltage • f = frequency • Ishort = short-circuit current during switching •  = duration of short-circuit current • Ileak = leakage current • Why averages don’t work:Bursty behavior, dI/dt, peak current, temperature

  8. Better Simulation Method P = ACVDDVswingf + AVIshort + Vileak sum over all blocks

  9. Typical Power-Performance Modeling Technologyparameters Micro-arch.config Staticpower model Cycle-accurateperformancemodel Dynamicpowerestimation cycle-by-cyclestatistics activityfactors

  10. Metrics • What metrics do we care about? • Execution time [sec or IPC] • Energy (battery life) [W] • Energy-delay product [Ws] • Energy-delay2 product? [Ws2] • Power density (temperature) [W / mm2] • Temperature [K or °C] • DI/dt • Peak power dissipation • Might also care about these at finer granularities:micro-arch. blocks, decoders, circuits, etc.

  11. Basic Techniques for Power Efficiency • Leakage: turn things off (but you lose the data) • Resize structures • Turn off idle structures • Turn off entries, eg cache decay • 4T RAM cells? • Dynamic: reduce activity factors • Clock gating • Resize structures • “Utility” predictor • Throttle processor width (eg, fetch width) • Filter caches • Datapath resizing • etc. P = ½ACV2f + AVIshort + VIleak

  12. Simulating Power for Greater Accuracy P = ½ACV2f + AVIshort + VIleak In all these cases, we want to find relevant power-related parameters (esp. effective switching capacitance C)-- performance model provides A • More detailed block-specific power information(circuit design style, etc.) • Switching activity (Hamming distance) • Interconnect (floorplanning, approx. area) • Clock tree (H-tree vs. balanced H-tree) • Random logic (empirical models) • Busses, transactions, durations (pull-up/pull-down/hi-Z, read vs. write, etc.)

  13. Simulation Challenges • Need leakage models - f(T) • Need temperature models • Eventually want to integrate all these into a fast simulator • This is a research challenge in its own right

More Related