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A new algorithm for technology mapping of Lookup Table-based Field-Programmable Gate Arrays is presented. It has the capability of producing slightly more compact designs than some existing mappers, and more significantly the flexibility of trading routability with compactness of a design. W e have implemented the algorithm in the h a p program, and compared its routability with two other mappers. h a p can produce mappings with better routability characteristics, and more significantly h a p produces routable mappings when other mappers do not.

We examine empirically the performance of multi-level logic minimization tools for a lookup table-based Field-Programmable Gate Array (FPGA) technology. The experiments are conducted by using the university tools misII for combinational logic minimization and mustang for state assignment, and the industrial tools xnfmap for technology mapping and apr for automatic placement and routing. We measure the quality of the multi-level logic minimization tools by the number of routed con gurable logic blocks (CLBs) in the FPGA realization. We report three results: a) there is a linear relationship between the number of literals and the number of routed CLBs, and b) in all 34 MCNC-89 benchmark nite state machines, one-hot state assignment resulted in substantially less CLBs than any other state encoding methods available in mustang, c) we present a delay model to provide routing delay prediction based on fanout, and apply the model to estimate the delays of the FPGA implementation of logic expressions prior to technology mapping, place and route. These results are useful for prototyping a design in FPGAs, and then transferring the design to a di erent technology (e.g., CMOS standard cell). It provides valuable information on the di erence in performance of a design realized in di erent technologies.

Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This article uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm. The model constructed here permits any marginal distribution for the series and the most flexible autocorrelations possible, including those with negative dependence. Likelihood methods of inference are explored. The article first develops the modeling methods, which entail a discrete transformation of a Gaussian process having seasonal dynamics. Properties of this model class are then established and particle filtering likelihood methods of parameter estimation are developed. A simulation study demonstrating the efficacy of the methods is presented and an application to the number of rainy days in successive weeks in Seattle, Washington is given.

This paper develops a mathematical model and statistical methods to quantify trends in presence/absence observations of snow cover (not depths) and applies these in an analysis of Northern Hemispheric observations extracted from satellite flyovers during 1967-2021. A two-state Markov chain model with periodic dynamics is introduced to analyze changes in the data in a cell by cell fashion. Trends, converted to the number of weeks of snow cover lost/gained per century, are estimated for each study cell. Uncertainty margins for these trends are developed from the model and used to assess the significance of the trend estimates. Cells with questionable data quality are explicitly identified. Among trustworthy cells, snow presence is seen to be declining in almost twice as many cells as it is advancing. While Arctic and southern latitude snow presence is found to be rapidly receding, other locations, such as Eastern Canada, are experiencing advancing snow cover.