Name
Abstract | ||
|---|---|---|
A new approach for high-level power estimation is presented which considers the increasingly important effect of glitching. The approach is based on modeling of functional building blocks by pre-characterization. This way the accuracy of modeling at the circuit level can be preserved to higher levels of abstraction. New metrics employing probability functions are applied to quantitatively describe the glitch generation and transfer characteristic as well as the power induced by glitching. These empirical probability functions are approximated using superimposed standard distributions in order to significantly reduce the number of involved parameters. In this contribution it is investigated what maximum accuracy can be achieved by this approach when tolerating a complex modeling step. As the models resulting from pre-characterization can be reused in other designs, larger modeling efforts are acceptable. Based on the evaluation of the essential part of array multipliers it is shown that the presented approach is suitable to predict the glitching part of power consumption with good accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VLSID.2014.78 | VLSI Design |
Keywords | Field | DocType |
complex modeling step,glitching,glitch generation,power consumption,larger modeling effort,integrated circuit modelling,dynamic power consumption,empirical probability function,power estimation,statistical analysis,glitching part,maximum accuracy,dynamic power consumption estimation,statistical modeling,glitching effects,high-level power estimation,macro-model,good accuracy,probability functions,rtl power estimator,essential part,new approach,transfer characteristic,probability | Glitch,Computer science,Empirical probability,Electronic engineering,Dynamic demand,Statistical model,Statistical analysis,Power consumption | Conference |
ISSN | Citations | PageRank |
1063-9667 | 1 | 0.38 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Meixner | 1 | 7 | 2.57 |
| Tobias G. Noll | 2 | 199 | 37.51 |