Upload
darrell-lamb
View
215
Download
0
Tags:
Embed Size (px)
Citation preview
Modeling and Estimation of Full-Chip Modeling and Estimation of Full-Chip Leakage Current Considering Within-Leakage Current Considering Within-
Die CorrelationsDie Correlations
Khaled R. Heloue, Navid Azizi, Farid N. Najm
University of Toronto
{khaled,nazizi,najm}@eecg.utoronto.ca
2
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
3
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
4
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)
5
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
6
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
7
High-level Characteristics
8
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
9
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 + σ2
wd
10
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),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
11
Correlation Function
12
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
2cLbLaeX
13
Leakage Fitting – “Good”
14
Leakage Fitting – “bad”
15
Histogram: MC vs Analytical
16
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(.)
rfr nmnm ,,
17
Leakage Correlation: MC vs Analytical
For all pairs of cells (m,n), we found that leakage correlation is approximately equal to the channel length correlation
18
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
19
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?
20
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)
21
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)
22
Leakage of RG
Mean:
Variance:
Covariance:
rfrCp
m
p
nnmnmnm
i
p
iiii
p
ii
i
p
ii
1 1,
2
1
22
1
2
1
23
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, 2, and (r)
Then we can determine the full-chip leakage meanand variance
24
Full-chip Leakage Estimation
Assume that (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:
D
I
I
drAPrrrd
n
0
2222 2
26
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
27
Results
28
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)
29
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)
c499 c1355 c432 c1908 c880 c2670 c5315 c7552 c6288
1.04% 0.41% 1.14% 0.36% 0.74% 0.52% 0.23% 0.34% 1.38%
30
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
31
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.