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Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation

Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation. Min Ni and Seda Ogrenci Memik November 6, 2006. EECS Department, Northwestern University, Evanston. Thermal Leakage Coupling. Four main sources of leakage current Reverse-biased junction leakage current (IREV)

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Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation

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  1. Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation Min Ni and Seda Ogrenci Memik November 6,2006 EECS Department, Northwestern University, Evanston

  2. Thermal Leakage Coupling • Four main sources of leakage current • Reverse-biased junction leakage current (IREV) • Gate induced drain leakage (IGIDL) • Gate direct tunneling leakage (IG) • Subthreshold (weak inversion) leakage (Isub)

  3. Thermal Leakage Coupling • Power consumption as a function of temperature [Pedram06]:

  4. Previous Work • Low Power Resource Binding • [Chang, DAC95], [Chang, DAC96] • Temperature-aware Resource Binding [Mukherjee, DAC05] • Given Resource Constraint • Given Peak Temperature Constraint

  5. Motivation • Question: how to decide the peak temperature constraint in high-level synthesis? • One possible metric is minimizing the total leakage power • Concept: two-state low power design • Phase one: low leakage power resource allocation • Phase two: low dynamic power resource binding • Modeling: relation between number of resources n, temperature T and total leakage power Pleakage • Solution: find the number of resources, hence, temperature that minimizes the total leakage power

  6. Outline • Leakage estimation model • Curve fitting • Heat transfer for leakage estimation • Redundant resource allocation • Resource dynamic power • Estimating the package properties • Steady state temperature • Experimental results

  7. Leakage Modeling • Analytic Model • Curve fitting • Exact Lagrange’s interpolation:

  8. Leakage Modeling • Benefit vs. Analytic: • Polynomial is better for analytical and numerical computation • Let HSpice take care of the physical details • Benefit vs. non-exact fitting, e.g. least-square • Exact fitting over the range of interest

  9. Heat Transfer Modeling • The basic relation between power density, heat transfer coefficient and temperature [Im, IEDM00] • Temperature evaluation based on dynamic power, which assumes to be a constant value

  10. Heat Transfer Modeling • Actual power • The situation becomes more complicated after adding the leakage power • Leakage power scaling based on the area of resource • F = 250 for 16-bit multiplier with area = 2107.54 • F = 80 for 32-bit adder with area = 665.77

  11. Optimal Resource Number • The relation between the number of resources and total leakage power • If we set , we have,

  12. What’s Next • Given the number of resources n, the subproblem becomes solving the following equation • Here, we still have two unknown values • Dynamic power Pd • Heat transfer coefficient h • Our goal is to decide n, which minimizes the following • Pleakage = n*Lp(Tx)

  13. Resource Dynamic Power Estimation • Assumptions and simplifications • Each resource consumes a typical average dynamic power for executing one operation • Ignore the dynamic power of extra dynamic power of MUX when sharing resource • Dynamic power of one operation is

  14. Estimating the Package Properties • Tradeoff between heat transfer coefficient and cost • Thermal runaway • Maximum h (minimum cost) package • Find the maximum h • Binary search • Two Initial points

  15. Steady State Temperature • Solve the following equation by secant method • Secant method, no explicit derivative is needed • Initial point

  16. Complete flow of algorithm • Incremental search • The solution space is small • Near-optimal solution • The leakage benefit becomes small • Optimize when more than one resource type is in the DFG • First add redundancy for the module with highest power density • The operations are assumed to be distributed evenly among all available resources

  17. Experiment results • Resources used in the experiments • Scaling from 180nm down to 70nm by full-scale methodology • Benchmarks are popular DSP and multimedia kernels [Mangione-Smith, Micro97], example “arf”,

  18. Experiment Results • Leakage power vs. min-resource allocation(53.8% improvement) and temperature-aware allocation(35.7% improvement) [Mukherjee, DAC05]

  19. Experiment Results • Resource temperature of different allocation strategies • Adder and multiplier temperature

  20. Conclusions • The contribution of this paper includes: • A paradigm for two-stage low power resource allocation and binding methodology • A simple leakage estimation model in high-level synthesis design phase • A leakage optimizing algorithm trading off resource area with total leakage power Thank you

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