14 research outputs found

    A design flow for performance planning : new paradigms for iteration free synthesis

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    In conventional design, higher levels of synthesis produce a netlist, from which layout synthesis builds a mask specification for manufacturing. Timing anal ysis is built into a feedback loop to detect timing violations which are then used to update specifications to synthesis. Such iteration is undesirable, and for very high performance designs, infeasible. The problem is likely to become much worse with future generations of technology. To achieve a non-iterative design flow, early synthesis stages should use wire planning to distribute delays over the functional elements and interconnect, and layout synthesis should use its degrees of freedom to realize those delays

    A Design Flow for Performance Planning: New Paradigms for Iteration Free Synthesis

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    Design Planning

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    Automatic wiring design

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    Floorplan design using annealing

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    An application of simulated annealing to floorplan design or macro placement is described. It uses a minimum size configuration space without invalid solutions, a constant object function, an adaptive control schedule, and an indicator for proper convergence. Fast convergence and improved flexibility are the salient features of this approach

    Implementierung von Simulated Annealing auf Transputer-Systemen

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    Genetic Optimization of Fuzzy Classification Systems - A Case Study

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    This contribution presents a fuzzy method for a particular kind of pixel classification. It is one of the most important results of the development of an inspection system for a silk-screen printing process. The classification algorithm is applied to a reference image in the initial step of the printing process in order to obtain regions which are to be checked by applying different criteria. Tight limitations in terms of computation speed have necessitated very specific, efficient methods which operate locally. These methods are motivated and described in detail in the following. Furthermore, the optimization of the parameters of the classification system with enetic algorithms is discussed. Finally, the genetic approach is compared with other probabilistic optimization methods
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