The LogiCORE™ GTP Wizard automates the task of creating HDL wrappers to configure the high-speed serial GTP transceivers in Virtex™-5 LXT and SXT devices. The menu-driven interface allows one or more GTP transceivers to be configured using pre-definedtemplates for popular industry standards, or from scratch, to support a wide variety of custom protocols.The Wizard produces a wrapper, an example design, and a testbench for rapid integration and verification of the serial interface with your custom function
Features• Creates Customized HDL wrappers to configureVirtex-5 RocketIO™ GTP transceivers• Users can configure Virtex-5 GTP transceivers toconform to industry standard protocols usingpredefined templates, or tailor the templates forcustom protocols• Included protocol templates provide support for thefollowing specifications: Aurora, CPRI, FibreChannel 1x, Gigabit Ethernet, HD-SDI, OBSAI,OC3, OC12, OC48, PCI Express® (PCIe®), SATA,SATA II, and XAUI• Automatically configures analog settings• Each custom wrapper includes example design, testbench; and both implementation and simulation scripts
標(biāo)簽：
Transceiver
Virtex
Wizar
GTP
上傳時間：
2013-10-20
上傳用戶：dave520l
Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of Customized
methods and meta-algorithms
標(biāo)簽：
Optimization and Uncertainty Quantification
上傳時間：
2016-04-08
上傳用戶：huhu123456
