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Modeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlations. Khaled R. Heloue, Navid Azizi, Farid N. Najm University of Toronto {khaled,nazizi,najm}@eecg.utoronto.ca. Introduction.

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modeling and estimation of full chip leakage current considering within die correlations

Modeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlations

Khaled R. Heloue, Navid Azizi, Farid N. Najm

University of Toronto

{khaled,nazizi,najm}@eecg.utoronto.ca

introduction
Introduction
  • Leakage current has been increasing and, in some cases, has become the design limiter
  • Statistical process variations (mainly L and Vth) make leakage statistical in nature
    • Interested in the mean and variance of the chip leakage
    • Leakage is also state-dependent, but not too strongly so
  • Large leakage variance leads to chip yield loss
    • Performance may vary by 30% but leakage varies by 5X
    • Thus, leakage may become more yield-limiting than delay
  • During process & chip design, we need to control the leakage spread, i.e., to minimize the leakage variance
low leakage design
Low-Leakage Design
  • By design:
    • process development
    • Body-bias
    • Sleep transistors and multiple voltage islands
    • Low-leakage libraries (circuit design)
    • Drowsy states, etc.
  • Most of this is standard practice today
  • How can EDA help further manage the leakage?
    • EDA should be able to accurately model and estimate full-chip leakage statistics to empower low-leakage design
    • This option should be available at an early or a late stageof design
background
Background
  • Full-chip leakage estimation is useful at different points in the design flow:
    • Early estimation: given limited information about the design
      • Useful for design planning (power budgeting)
    • Late estimation: complete netlist, possibly circuit placement
      • Useful for final sign-off
  • Work on “early estimation”:
    • Narendra et al. & Rao et al.
      • Did not handle logic-gate/transistor topologies and/or within-die correlation
  • Work on “late estimation”:
    • Chang et al. & Agarwal et al.
      • O(n2) complexity (some refinements at the expense of accuracy)
full chip leakage model
Full-chip Leakage Model
  • We propose a “Full-chip Leakage Estimation Model” that considers:
    • Logic-gate structures and transistor topologies
    • Die-to-Die & Within-Die variations
    • Within-Die correlation
  • Our model has the following features:
    • Accurate
    • Computationally efficient (constant-time)
    • Can be used early or late in the design flow
hypothesis
Hypothesis
  • Hypothesis:
    • Certain “high-level characteristics” of a candidate chip design are sufficient to determine its leakage statistics
    • All designs that share the same values of these high-level characteristics have approximately the same leakage,for large gate count
  • Hypothesis confirmed by results
early estimation vs late estimation
Early Estimation vs Late Estimation
  • Whether in Early or Late modes, the inputs to our model are the same
    • Shown in previous slide
  • Only difference is how the “Design Information”is obtained:
    • In Early mode:
      • number of gates, frequency of cell usage, and dimension of layout are either “specified” or “expected” based on design experience
    • In Late mode:
      • number of gates, frequency of cell usage, and dimension of layout are “extracted” from the fully specified design
process information
Process Information
  • We focus on leakage variations due to channel length (L) variations
    • The effect of Vth variations on the leakage mean is known (multiplicative term)
    • The effect of Vth variations on the leakage variance is negligible compared to L
  • We assume that the mean (μ) and standard deviation (σ) of L are known
  • Die-to-die and within-die variances of L are also known

σ2 = σ2dd + σ2wd

process information1
Process Information
  • Channel length L variations are correlated due to:
    • Die-to-die (D2D) variations are totally correlated
    • Within-die (WID) variations are spatially correlated
  • We assume that the WID correlation function,r(r),for L is known
    • It gives the correlation coefficient between the lengths of two devices separated by a distance r
  • Total length correlation (D2D + WID) can be easily obtained
library information
Library Information
  • Our leakage model works for standard cell type designs
    • A library of p standard cells is available
  • Characterize every cell in the library for leakage (mean and variance) using one of two methods:
    • Monte-Carlo (MC) analysis, by varying L
      • Good accuracy, costly
    • Analytical method, by fitting leakage (X) into functional form, and determine analytically the exact leakage mean and variance
      • Less accurate, cheap
  • Result: mean (μi) and standard deviation (σi) of leakage for every cell in the library, i = 1, …, p
leakage correlation
Leakage Correlation
  • We previously assumed that channel length correlation is available from the foundry
  • What about leakage correlation?
    • Leakage correlation depends on:
      • Distance separating cells
      • Types of cells
  • Using the fitted functional form for cell leakage:
    • We can determine analytically the leakage correlation between gates of types m and n, where m,n = 1, …, p, given channel length correlation. We call it a mapping fm,n(.)
leakage correlation mc vs analytical
Leakage Correlation: MC vs Analytical
  • For all pairs of cells (m,n), we found thatleakage correlation is approximately equal to the channel length correlation
design information
Design Information
  • Information about the actual design:
    • Expected/extracted number of cells in the design
      • n cells
    • Expected/extracted frequency of usage of cells in the library
      • for cell i, αi = ni /n
    • Expected/extracted dimensions of the layout area (chip core)
      • Width W and Height H
full chip model
Full-chip Model
  • The full-chip model
    • a rectangular array of dimensions H and W
    • n identical sites, where n is the total number of gates
    • Each site is occupied by a Random Gate (RG)
  • What is a Random Gate?
random gate
Random Gate
  • Similar to a RV, a RG takes as instances or outcomes gates from the standard-cell library
  • We require the discrete probability distribution of the RG to be identical to the frequency of cell usage
    • P{ RG= gate i } = αi for i = 1, … , p
  • Based on the RG, the Full-chip model is a template for all designs that share the same high-level characteristics
    • It covers the set of all such designs (recall hypothesis)
    • We’ll show that this set converges (in terms of leakage)
leakage of rg
Leakage of RG
  • If the leakage statistics of the RG are defined,Full-chip leakage estimation is possible
    • Need:mean, variance, and correlation (or covariance) of RG
  • These will depend on:
    • Frequency of cell usage (design information)
    • means and variances of leakage of cells (library information)
    • Channel length and Leakage correlation (process information)
leakage of rg1
Leakage of RG
  • Mean:
  • Variance:
  • Covariance:
full chip leakage estimation
Full-chip Leakage Estimation
  • Recall the full-chip model is as an array of generic “sites” to be occupied by RGs
  • We determined the mean, variance, and correlation of the RG leakage
    • Call them μ,s2, and r(r)
  • Then we can determine the full-chip leakage meanand variance
full chip leakage estimation1
Full-chip Leakage Estimation
  • Assume that r(r) goes to zero at a distance D where D is less than the chip core height H and width W
    • Focus on within-die variations, for simplicity of presentation
  • Let P be the chip core perimeter, and A its area
  • Let d be the logic gate density per unit area (e.g. n/A)
  • Then, the full-chip leakage mean and variance are given by:
confirming hypothesis test plan
Confirming Hypothesis: Test plan
  • Consider a range of target gate counts
  • For a given # gates
    • Generate many circuits that share the same high-level characteristics (satisfy the cell usage frequencies, etc…)
    • For each circuit
      • Place it
      • Use Monte Carlo on parameters to generate leakage distribution
      • Measure the error in mean and standard deviation relative to our estimate (Integral)
    • Find the maximum/min error over all circuits
    • Plot the two error extremes against that gate count
  • See plot on next slide
confirming hypothesis
Confirming Hypothesis
  • Two conclusions from plot:
    • First, the high-level characteristics of a design(which drive our model) are sufficient to determineaccurately its leakage statistics
    • Second, the set of (possibly different) designs that share the same high-level characteristics have approximately the same leakage, for large gate count
  • Note that this is an example of early estimation(high-level characteristics were specified a priori)
late estimation
Late Estimation
  • We have also tested our model as a full-chip leakage late estimator
    • Synthesized, placed, and routed ISCAS85 benchmark circuits
    • Extracted the sufficient high-level characteristics
    • Used our model to predict leakage and compared results to MC sampling
      • Listed error in standard deviation (error in mean is negligible)
conclusion
Conclusion
  • Full-chip leakage estimation is possible both at anEarly or a Late stage:
    • Based on concept of Random Gate
    • Has been verified for standard-cell type layouts
    • For large gate count, accuracy is very good
  • High-level characteristics of design are all that matters:
    • Standard Cell leakage mean and variance
    • Cell usage frequencies
    • Leakage correlation function
    • Chip core area and perimeter (dimensions)
    • Number of cells in the design
  • Further work is required to handle both timing and leakage in a single estimator
bibliography
Bibliography
  • Siva Narendra, Vivek De, Dimitri Antoniadis, and Anantha Chandrakasan. Full-chip sub-threshold leakage power prediction model of sub-0.18μm CMOS. IEEE/ACM International Symposium on Low Power Electronics and Design, 2002.
  • Rajeev Rao, Ashish Srivastava, David Blaauw, and Dennis Sylvester. Statistical analysis of sub-threshold leakage current for VLSI circuits. IEEE Transactions on VLSI Systems, 12(2):131–139, February 2004.
  • Hongliang Chang and Sachin S. Sapatnekar. Full-chip analysis of leakage power under process variations, inlcuding spatial correlations. IEEE Design Automation Conference, 2005.
  • Amit Agarwal, Kunhyuk Kang, and Kaushik Roy. Accurate estimation and modeling of total chip leakage considering inter-& intra-die process variations. IEEE International Conference on Computer-aided Design, 2005.