Most design automation systems attempt to automate only a small part of the physical description. Much is left to the designers. An exploratory design automation system is being developed at the University of Southern...
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The problem of finding the minimum amount of fanout needed to realize a switching function f is investigated. Fanout-free functions are defined, and necessary and sufficient conditions for a function to be fanout-free...
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The problem of finding the minimum amount of fanout needed to realize a switching function f is investigated. Fanout-free functions are defined, and necessary and sufficient conditions for a function to be fanout-free are derived. A measure τ(f) called the input fanout index, is introduced which represents the minimum number of input variables that fan out in any realization off. It is shown that τ(f), can be determined from the prime implicants and implicates off. Another measure of fanout µ(f), which is the minimum number of signal lines that must fan out in any network realizing f is defined, and some of the properties of µ(f) are examined.
This volume contains the 37 papers presented at the 9th International Con- rence on Real-Time and Embedded Computing Systems and Applications (RT- CSA 2003). RTCSA is an international conference organized for scientis...
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ISBN:
(数字)9783540246862
ISBN:
(纸本)9783540219743
This volume contains the 37 papers presented at the 9th International Con- rence on Real-Time and Embedded Computing Systems and Applications (RT- CSA 2003). RTCSA is an international conference organized for scientists and researchers from both academia and industry to hold intensive discussions on advancing technologies topics on real-time systems, embedded systems, ubiq- tous/pervasive computing, and related topics. RTCSA 2003 was held at the Department of electricalengineering of National Cheng Kung University in Taiwan. Paper submissions were well distributed over the various aspects of real-time computing and embedded system technologies. There were more than 100 participants from all over the world. The papers, including 28 regular papers and 9 short papers are grouped into thecategoriesofscheduling,networkingandcommunication,embeddedsystems, pervasive/ubiquitous computing, systems and architectures, resource mana- ment, ?le systems and databases, performance analysis, and tools and de- lopment. The grouping is basically in accordance with the conference program. Earlier versions of these papers were published in the conference proceedings. However, some papers in this volume have been modi?ed or improved by the authors, in various aspects, based on comments and feedback received at the conference. It is our sincere hope that researchers and developers will bene?t from these papers. We would like to thank all the authors of the papers for their contribution. We thank the members of the program committee and the reviewers for their excellent work in evaluating the submissions. We are also very grateful to all the members of the organizing committees for their help, guidance and support.
Three abstract optimization problems are presented along with doubly iterative algorithms for their numerical solution. These algorithms are generalizations of particular algorithms described by Barr and Gilbert [19],...
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Three abstract optimization problems are presented along with doubly iterative algorithms for their numerical solution. These algorithms are generalizations of particular algorithms described by Barr and Gilbert [19], [21] and Fujisawa and Yasuda [22]. The supporting theory is fully developed along with proofs of convergence. Practical aspects of computations are considered and procedures which insure rapid convergence are discussed. Two applications to discrete-time optimal control problems are described.
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representa...
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.
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