NDLI: Statistical timing based on incomplete probabilistic descriptions of parameter uncertainty

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Timing driven power gating

Statistical timing based on incomplete probabilistic descriptions of parameter uncertainty

Content Provider	ACM Digital Library
Author	Wang, Wei-Shen Kreinovich, Vladik Orshansky, Michael
Abstract	Existing approaches to timing analysis under uncertainty are based on restrictive assumptions. Statistical STA techniques assume that the full probabilistic distribution of parameter uncertainty is available; in reality, the complete probabilistic description often cannot be obtained. In this paper, a new paradigm for parameter uncertainty description is proposed as a way to consistently and rigorously handle partially available descriptions of parameter uncertainty. The paradigm is based on a theory of interval probabilistic models that permit handling uncertainty that is described in a distribution-free mode - just via the range, the mean, and the variance. This permits effectively handling multiple real-life challenges, including imprecise and limited information about the distributions of process parameters, parameters coming from different populations, and the sources of uncertainty that are too difficult to handle via full probabilistic measures (e.g. on-chip supply voltage variation). Specifically, analytical techniques for bounding the distributions of probabilistic interval variables are proposed. Besides, a provably correct strategy for fast Monte Carlo simulation based on probabilistic interval variables is introduced. A path-based timing algorithm implementing the novel modeling paradigm, as well as handling the traditional variability descriptions, has been developed. The results indicate the proposed algorithm can improve the upper bound of the 90th-percentile circuit delay, on average, by 5.3% across the ISCAS'85 benchmark circuits, compared to the worst-case timing estimates that use only the interval information of the partially specified parameters.
Starting Page	161
Ending Page	166
Page Count	6
File Format	PDF
ISBN	1595933816
DOI	10.1145/1146909.1146954
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-07-24
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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