Residue arithmetic for variation-tolerant design of multiply-add units
PATMOS'09 Proceedings of the 19th international conference on Integrated Circuit and System Design: power and Timing Modeling, Optimization and Simulation
| Hi-index | 0.00 |
SSTA requires accurate statistical distribution models of non-Gaussian random variables of process parameters andtiming variables. Traditional quadratic Gaussian model has been shown to have some serious limitations. In particular, it limits the range of skewness that can be modeled and it can not model the kurtosis. In this paper, we presented complex-coefficient quadratic Gaussian polynomial model and higher order Gaussian polynomial model to resolve these difficulties. Experimental results show how our methods and new algorithms expose some enhancements in both accuracy and versatility.