ACM Home Page
Please provide us with feedback. Feedback
Statistical optimization of leakage power considering process variations using dual-Vth and sizing
Full text PdfPdf (389 KB)
Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 41st annual Design Automation Conference table of contents
San Diego, CA, USA
SESSION: Leakage power optimization table of contents
Pages: 773 - 778  
Year of Publication: 2004
ISBN:1-58113-828-8
Authors
Ashish Srivastava  University of Michigan, Ann Arbor, MI
Dennis Sylvester  University of Michigan, Ann Arbor, MI
David Blaauw  University of Michigan, Ann Arbor, MI
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 132,   Citation Count: 28
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/996566.996775
What is a DOI?

ABSTRACT

Increasing levels of process variability in sub-100nm CMOS design has become a critical concern for performance and power constraint designs. In this paper, we propose a new statistically aware Dual-Vt and sizing optimization that considers both the variability in performance and leakage of a design. While extensive work has been performed in the past on statistical analysis methods, circuit optimization is still largely performed using deterministic methods. We show in this paper that deterministic optimization quickly looses effectiveness for stringent performance and leakage constraints in designs with significant variability. We then propose a statistically aware dual-Vt and sizing algorithm where both delay constraints and sensitivity computations are performed in a statistical manner. We demonstrate that using this statistically aware optimization, leakage power can be reduced by 15-35% compared to traditional deterministic analysis. The improvements increase for strict delay constraints making statistical optimization especially important for high performance designs.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
S. Duvall, "Statistical Circuit Modeling and Optimization," Workshop on statistical metrology, pp.56--63, 2000.
 
2
S. Nassif, "Delay variability: sources, impacts and trends," Proc. ISSCC, pp. 368--369, 2000.
 
3
K. Bowman et al., "Impact of die-to-die and within-die Parameter Fluctuations on the Maximum Clock Frequency Distribution for Gigascale Integartion", IEEE J. Solid-State Circuits, pp.183--190, Feb. 2002.
 
4
5
6
7
8
 
9
P. Feldmann and S. Director, "Accurate and efficient evaluation of Circuit yield and yield gradients", Proc. ICCAD, pp.120--123, 1990.
 
10
S. Director, et al., Optimization of Parametric yield: tutorial," Proc. CICC, 1992.
11
 
12
13
14
 
15
Ruchir Puri, personal communication.
16
 
17
 
18
L. Wei, K. Roy, and C. Koh, "Power minimization by simultaneous dual-Vth assignment and gate sizing," Proc. CICC, pp.413--416, 2000.
19
20
21
 
22
T. Sakurai and A.R. Newton, "Alpha-power law MOSFET model and its application to CMOS inverter delay and other formulas," IEEE J. Solid-State Circuits, pp.584--593, April 1990.
 
23
S.C. Schwartz and Y.S. Yeh, "On the distribution function and moments of power sums with lognormal components," Bell Systems Technical Journal, vol.61, pp.1441--1462, Sept. 1982.
 
24
 
25
J. Fishburn and A. Dunlop, "TILOS: a posynomial programming approach to transistor sizing", Proc. ICCAD, pp.326--328, 1985.
 
26
F. Brglez and H. Fujiwara. "A neutral netlist of 10 combinatorial benchmark circuits," Proc. ISCAS, 1985, pp.695--698.
27

CITED BY  28

Collaborative Colleagues:
Ashish Srivastava: colleagues
Dennis Sylvester: colleagues
David Blaauw: colleagues