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Xilinx UltraScale:The Next-Generation Architecture for Your Next-Generation Architecture
The Xilinx® UltraScale™ architecture delivers unprecedented levels of integration and capability with ASIC-class system- level performance for the most demanding applications.
The UltraScale architecture is the industr y's f irst application of leading-edge ASIC architectural enhancements in an All Programmable architecture that scales from 20 nm planar through 16 nm FinFET technologies and beyond, in addition to scaling from monolithic through 3D ICs. Through analytical co-OPTIMIZATION with the X ilinx V ivado® Design Suite, the UltraScale architecture provides massive routing capacity while intelligently resolving typical bottlenecks in ways never before possible. This design synergy achieves greater than 90% utilization with no performance degradation.
Some of the UltraScale architecture breakthroughs include:
• Strategic placement (virtually anywhere on the die) of ASIC-like system clocks, reducing clock skew by up to 50%
• Latency-producing pipelining is virtually unnecessary in systems with massively parallel bus architecture, increasing system speed and capability
• Potential timing-closure problems and interconnect bottlenecks are eliminated, even in systems requiring 90% or more resource utilization
• 3D IC integration makes it possible to build larger devices one process generation ahead of the current industr y standard
• Greatly increased system performance, including multi-gigabit serial transceivers, I/O, and memor y bandwidth is available within even smaller system power budgets
• Greatly enhanced DSP and packet handling
The Xilinx UltraScale architecture opens up whole new dimensions for designers of ultra-high-capacity solutions.
Finite state machines are widely used in digital circuit designs. Generally, when designing a state machine using an HDL, the synthesis tools will optimize away all states that cannot be reached and generate a highly optimized circuit. Sometimes, however, the OPTIMIZATION is not acceptable. For example, if the circuit powers up in an invalid state, or the circuit is in an extreme working environment and a glitch sends it into an undesired state, the circuit may never get back to its normal operating condition.
最新的支持向量機工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal OPTIMIZATION", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
-PID controller has been extensively
used in the industrial world. But in this controller it is difficult to tune the PID gains.
We apply the genetic algorithm(GA) to tune the PID gains. The GA is an OPTIMIZATION algorithm using the biotic genetics.
The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative OPTIMIZATION of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
The cable compensation system is an experiment system that performs simulations of partial or microgravity environments on earth. It is a highly nonlinear and complex system.In this paper, a network based on the theory of the Fuzzy Cerebellum Model Articulation Controller(FCMAC) is proposed to control this cable compensation system. In FCMAC ,without appropriate learning rate, the control system based on FCMAC will become unstable or its convergence speed will become slow.In order to guarantee the convergence of tracking error, we present a new kind of OPTIMIZATION based on adaptive GA for selecting learning rate.Furthermore, this approach is evaluated and its performance is discussed.The simulation results shows that performance of the FCMAC based the proposed method is stable and more effective.
in this paper,wo propose an extension of the zerotree-based space-frequency quantization algorithm by adding a wedgelet symbol to its tree-pruning OPTIMIZATION.
Beginning with an overview of SQL Server 2000, this book discusses online transaction processing (OLTP) and online analytical processing (OLAP), features a tour of different SQL Server releases, and offers a guide to installation. The author describes and demonstrates the changes since SQL Server 7.0, thoroughly exploring SQL Server 2000 s capacity as a Web-enabled database server. Readers are then immersed in advanced database administration topics such as performance OPTIMIZATION and debugging techniques.
