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Variability driven gate sizing for binning yield optimization
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 43rd annual Design Automation Conference table of contents
San Francisco, CA, USA
SESSION: Session 54: logic and sequential synthesis table of contents
Pages: 959 - 964  
Year of Publication: 2006
ISBN:1-59593-381-6
Authors
Azadeh Davoodi  University of Maryland, College Park, MD
Ankur Srivastava  University of Maryland, College Park, MD
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 31,   Citation Count: 11
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ABSTRACT

Process variations result in a considerable spread in the frequency of the fabricated chips. In high performance applications, those chips that fail to meet the nominal frequency after fabrication are either discarded or sold at a loss which is typically proportional to the degree of timing violation. The latter is called binning. In this paper we present a gate sizing-based algorithm that optimally minimizes the binning yield-loss. We make the following contributions: 1) prove the binning yield function to be convex, 2) do not make any assumptions about the sources of variability, and their distribution model, 3) we integrate our strategy with statistical timing analysis tools (STA), without making any assumptions about how STA is done, 4) if the objective is to optimize the traditional yield (and not binning yield) our approach can still optimize the same to a very large extent. Comparison of our approach with sensitivity-based approaches under fabrication variability shows an improvement of on average 72% in the binning yield-loss with an area overhead of an average 6%, while achieving a 2.69 times speedup under a stringent timing constraint. Moreover we show that a worst-case deterministic approach fails to generate a solution for certain delay constraints. We also show that optimizing the binning yield-loss minimizes the traditional yield-loss with a 61% improvement from a sensitivity-based approach.


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.

 
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CITED BY  11
 
 
 
 
 
 
 

Collaborative Colleagues:
Azadeh Davoodi: colleagues
Ankur Srivastava: colleagues