Behavioral synthesis from VHDL using structured modeling

This dissertation describes work in behavioral synthesis involving the development of a VHDL Synthesis System VSS which accepts a VHDL behavioral input specification and performs technology independent synthesis to generate a circuit netlist of generic components. The VHDL language is used for input and output descriptions. An intermediate representation which incorporates signal typing and component attributes simplifies compilation and facilitates design optimization.A Structured Modeling methodology has been developed to suggest standard VHDL modeling practices for synthesis. Structured modeling provides recommendations for the use of available VHDL description styles so that optimal designs will be synthesized.A design composed of generic components is synthesized from the input description through a process of Graph Compilation, Graph Criticism, and Design Compilation. Experiments were performed to demonstrate the effects of different modeling styles on the quality of the design produced by VSS. Several alternative VHDL models were examined for each benchmark, illustrating the improvements in design quality achieved when Structured Modeling guidelines were followed

Input noise approximation in tracker modeling

The validity of approximating random Gaussian distributed inputs used in human response modeling by sums of discrete sine waves is studied. An ideal rectangular power density spectrum is simulated using both filtered Gaussian white noise and sums-of-discrete sine waves with three different input cutoff frequencies in the same compensatory tracking task. Resulting normalized tracking error and quality operator observations are used to investigate apparent discrepancies in human operator characteristics. Results show that discrete and continuous input tracking data compare favorable when the power in the crossover region is taken into account

Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

Microsimulation is becoming increasingly important in traffic demand modeling. The major advantage over traditional four-step models is the ability to simulate each traveler individually. Decision-making processes can be included for each individual. Traffic demand is the result of the different decisions made by individuals; these decisions lead to plans that the individuals then try to optimize. Therefore, such microsimulation models need appropriate initial demand patterns for all given individuals. The challenge is to create individual demand patterns out of general input data. In practice, there is a large variety of input data, which can differ in quality, spatial resolution, purpose, and other characteristics. The challenge for a flexible demand-modeling framework is to combine the various data types to produce individual demand patterns. In addition, the modeling framework has to define precise interfaces to provide portability to other models, programs, and frameworks, and it should be suitable for large-scale applications that use many millions of individuals. Because the model has to be adaptable to the given input data, the framework needs to be easily extensible with new algorithms and models. The presented demand-modeling framework for large-scale scenarios fulfils all these requirements. By modeling the demand for two different scenarios (Zurich, Switzerland, and the German states of Berlin and Brandenburg), the framework shows its flexibility in aspects of diverse input data, interfaces to third-party products, spatial resolution, and last but not least, the modeling process itself

Input-Output Modeling

Input-output modeling activities have been sponsored by IIASA for many years. These activities provided a link between various research centers as well as associated research groups engaged in constructing similar systems, and also individual researchers interested in the application of input-output type models. The aim of the sixth IIASA meeting was to demonstrate to a wide community of researchers, policy makers and their advisers the likely uses of integrated input-output models in economic policy both at industrial and national levels. This message is well illustrated. The papers provide the users with illuminating simulation studies showing on the one hand the impact of technological structural changes in the macroaggregates and on the other hand how the changes in macrovariables influence the pattern of changes in particular industries. This volume presents the results of the meeting organized by IIASA and IES in Warsaw

Global sensitivity analysis of computer models with functional inputs

Global sensitivity analysis is used to quantify the influence of uncertain input parameters on the response variability of a numerical model. The common quantitative methods are applicable to computer codes with scalar input variables. This paper aims to illustrate different variance-based sensitivity analysis techniques, based on the so-called Sobol indices, when some input variables are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary meta-modeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked Generalized Linear Models (GLM) or Generalized Additive Models (GAM). The ``mean'' model allows to estimate the sensitivity indices of each scalar input variables, while the ``dispersion'' model allows to derive the total sensitivity index of the functional input variables. The proposed approach is compared to some classical SA methodologies on an analytical function. Lastly, the proposed methodology is applied to a concrete industrial computer code that simulates the nuclear fuel irradiation

INPUT-OUTPUT MODELING AND RESOURCE USE PROJECTION

Resource /Energy Economics and Policy,

The SLH framework for modeling quantum input-output networks

Many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, eg. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields by an operator triple $(S,L,H)$. Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.Comment: 60 pages, 14 figures. We are still interested in receiving correction