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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) - PowerPoint PPT Presentation
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EECS Department, Northwestern University, EvanstonEECS Department, Northwestern University, Evanston
Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation
Thermal-Induced Leakage Power Optimization by Redundant Resource Allocation
Min Ni and Seda Ogrenci MemikMin Ni and Seda Ogrenci Memik
November 6,November 6, 20062006
Thermal Leakage CouplingThermal 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)
Thermal Leakage CouplingThermal Leakage Coupling
Power consumption as a function of temperature [Pedram06]:
Previous WorkPrevious Work
Low Power Resource Binding [Chang, DAC95], [Chang, DAC96]
Temperature-aware Resource Binding [Mukherjee, DAC05] Given Resource Constraint Given Peak Temperature Constraint
MotivationMotivation
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
)(A
PPfT dl
),( TngPl
),( TngPl
cnA
OutlineOutline
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
Leakage ModelingLeakage Modeling
Analytic Model
Curve fitting Exact Lagrange’s interpolation:
Leakage ModelingLeakage 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
Heat Transfer ModelingHeat 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
A
PPhTT dl
a
Heat Transfer ModelingHeat 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
dl PPP
Optimal Resource NumberOptimal Resource Number
The relation between the number of resources and total leakage power
If we set , we have,
),(),( nTpnnTP lel
0)(
dn
ndPl
0)( dn
dT
dT
dpnnp le
le
What’s NextWhat’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)
Resource Dynamic Power EstimationResource 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
Bench #steps Pd per Add Pd per Multi
Arf 21 534.19 3446.26
Ewf 17 659.89 4257.15
Fdct 12 934.84 6030.96
Fft 15 747.87 4824.77
smTR
PPopt
0
Estimating the Package PropertiesEstimating 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
Steady State TemperatureSteady State Temperature
Solve the following equation by secant method
Secant method, no explicit derivative is needed
Initial point
Complete flow of algorithmComplete 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
Experiment resultsExperiment 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”,
Area Average Pd Delay
Adder 665.77 3472.00 0.619
Multiplier 2107.54 22399.00 1.227
Experiment Results Experiment Results
Leakage power vs. min-resource allocation(53.8% improvement) and temperature-aware allocation(35.7% improvement) [Mukherjee, DAC05]
Experiment ResultsExperiment Results
Resource temperature of different allocation strategies Adder and multiplier temperature
ConclusionsConclusions
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 youThank you