A Hierarchical Timing Simulation Model

A hierarchical timing simulation model has been developed to deal with VLSI designs at any level of representation. A set of physically based parameters are used to characterize the behavior and timing of a semantic design object (cell) independent of its composition environment. As cells are composed, the parameters of the composite cell can be determined from those of the component cells either analytically or by simulation. Based on this model, a behavior-level simulator has been developed and combined with other tools to form an integrated design system that fully supports the structured design methodology

A Neural Network Model of Spatio-Temporal Pattern Recognition, Recall and Timing

This paper describes the design of a self~organizing, hierarchical neural network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall {both from STM and from LTM) is performed with a learned rhythmical structure. The network, bearing similarities with ART (Carpenter & Grossberg 1987a), learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operation. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.National Science Foundation (IRI-9024877

Optimal Inseason Management Of Pink Salmon Given Uncertain Run Sizes And Declining Economic Value

Thesis (Ph.D.) University of Alaska Fairbanks, 2001This is a comprehensive study on the fishery and management system (including the inseason stock abundance dynamics, the purse seine fleet dynamics and the inseason management) of pink salmon (Oncorhynchus gorbuscha) in the northern Southeast Alaska inside waters (NSE). Firstly, we presented a hierarchical Bayesian modelling approach (HBM) for estimating salmon escapement abundance and timing from stream count data, which improves estimates in years when data are sparse by "borrowing strength" from counts in other years. We presented a model of escapement and of count data, a hierarchical Bayesian statistical framework, a Gibbs sampling estimation approach for posterior distributions, and model determination techniques. We then applied the HBM to estimating historical escapement parameters for pink salmon returns to Kadashan Creek in Southeast Alaska. Secondly, a simulation study was conducted to compare the performance of the HBM to that of separate maximum likelihood estimation of each year's escapement. We found that the HBM was much better able to estimate escapement parameters in years where few or no counts are made after the peak of escapement. Separate estimates for such years could be wildly inaccurate. However, even a single postpeak count could dramatically improve the estimability of escapement parameters. Third, we defined major stocks and their migratory pathways for the NSE pink salmon. We estimated the escapement timing parameters of these stocks by the HBM. A boxcar migration model was then used to reconstruct the catch and abundance histories for these stocks from 1977 to 1998. Finally, we developed a stochastic simulation model that simulates this fishery and management system. Uncertainties in annual stock size and run timing, fleet dynamics and both preseason and inseason forecasts were accounted for explicitly in this simulation. The simulation model was applied to evaluating four kinds of management strategies with different fishing opening schedules and decision rules. When only flesh quality is concerned, the present and a more aggressive strategy, both of which are adaptive to the run strength of the stocks, are able to provide higher quality fish without compromising the escapement objectives

A Hierarchical Timing Simulation Model for Digital Integrated Circuits and Systems

A hierarchical timing simulation model for digital MOS circuits and systems is presented. This model supports the structured design methodology, and can be applied to both "structure" and "behavior" representations of designs in a uniform manner. A simulator based on this model can run several orders of magnitude faster than any other simulators that offer the same amount of information. At the structure (transistor) level, the transient behavior of a digital MOS circuit is approximated by that of an RC network for estimating delays. The Penfield-Rubinstein RC tree model is extended to include the effects of parallel paths and initial charge distributions. As far as delay is concerned, a two-port RC network is characterized by three parameters: R: series resistance, C: loading capacitance and D: internal delay. These parameters can be determined hierarchically as networks are composed in various ways. The composition rules are derived directly from the Kirchoff's current and voltage laws, so that the consistency with physics is established. The (R, C, D) characterization of two-port RC networks is then generalized to describe the behavior of semantic cells at any level of representation. A semantic cell is a functional block which can be abstracted by its steady-state behavior to interface with other cells in the system. As semantic cells are composed, the parameters of the composite cell can be determined from those of the the component cells either analytically or by simulation. A Smalltalk implementation of the hierarchical timing simulation model is also presented