ACM Home Page
Please provide us with feedback. Feedback
Fast statistical timing analysis by probabilistic event propagation
Full text PdfPdf (98 KB)
Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 38th annual Design Automation Conference table of contents
Las Vegas, Nevada, United States
Pages: 661 - 666  
Year of Publication: 2001
ISBN:1-58113-297-2
Authors
Jing-Jia Liou  Electrical and Computer Engineering Department, University of California, Santa Barbara
Kwang-Ting Cheng  Electrical and Computer Engineering Department, University of California, Santa Barbara
Sandip Kundu  Intel Corporation, Austin
Angela Krstic  Electrical and Computer Engineering Department, University of California, Santa Barbara
Sponsors
EDAC : Electronic Design Automation Consortium
IEEE-CAS : Circuits & Systems
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 7,   Downloads (12 Months): 46,   Citation Count: 39
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/10.1145/378239.379043
What is a DOI?

ABSTRACT

We propose a new statistical timing analysis algorithm, which produces arrival-time random variables for all internal signals and primary outputs for cell-based designs with all cell delays modeled as random variables. Our algorithm propagates probabilistic timing events through the circuit and obtains final probabilistic events (distributions) at all nodes. The new algorithm is deterministic and flexible in controlling run time and accuracy. However, the algorithm has exponential time complexity for circuits with reconvergent fanouts. In order to solve this problem, we further propose a fast approximate algorithm. Experiments show that this approximate algorithm speeds up the statistical timing analysis by at least an order of magnitude and produces results with small errors when compared with Monte Carlo methods.


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
 
2
H.-F. Jyu, S. Malik, S. Devadas, and K. Keutzer. Statistical Timing Analysis of Combinational Logic Circuits. IEEE Transactions on VLSI Systems, 1(2):126-137, June 1993.
3
4
 
5
6
 
7
Synopsys. Design Compiler Reference Manual. May, 2000.
 
8

CITED BY  39

Collaborative Colleagues:
Jing-Jia Liou: colleagues
Kwang-Ting Cheng: colleagues
Sandip Kundu: colleagues
Angela Krstic: colleagues