SCI互连技术研究

采用一种适当的系统互连技术构建一个高带宽、低延迟、可伸缩的互连系统是当前并行，分布式处理的趋势。可伸缩一致性接口（SCI）是一种高性价比的系统互连解决方案。该文对SCI作了一些简单介绍，然后提出了一种嵌入式SCI互连系统。     

新型高性能互连技术Hyper Transport研究

HyperTransport(HT)是一种新型用于板级集成电路互连的、高性能、点到点、基于报文交换的互连技术。HyperTransport提供了用途广泛的连接，以减少系统内的总线数，为嵌入系统应用提供高性能的连接，可以实现高度可伸缩的多处理系统。对Hyper Transport的一些关键技术进行了介绍，并设计了一个HT的HOST桥。     

基于PCI-E接口的高速数字信号处理系统设计

为了满足目前实时信号处理要求,利用ADSP-TS201和Virtex4系列FPGA构建高速数字信号处理系统,采用点对点互连和紧耦合结构设计,利用FPGA自定义LVDS接口实现板级互连,通过PCI-E接口与上位机通信,具有良好的可扩展性。文章介绍了系统的结构、原理及PCB设计。     

光互连技术的探讨

讨论了电互连方式的缺陷以及光互连方式的优点，介绍了当前光互连的基本情况。通过对一个具体光互连模型的分析，论述了光互连的基本原理及光学实现技术。最后叙述了光互连模型实现ｎ维超立方的算法。     

基于DD-03282的ARINC429 PCI通信接口设计

介绍了一种基于HS3282的ARINC429 PCI通信接口板的设计方法，该设计应用DD-03282构建ARINC429总线通信系统，并使用FPGA实现与PC机互连的PCI接口逻辑。同时，还筒述了429通信接口板WDM驱动程序的编制。     

以存储器为中心的路由器MRouter

并行系统的互连技术一直是高性能计算机的一个关键研究领域。在本文作者1999年中提出了一种基于多端口快速存储器的新型互连体系结构MCIM,并在此基础上构造出规模为16－128个CPU的结点系统。该文将MCIM原理应用在互连网络通信技术上,实现了一种以存储器系统为中心 的路由器MRouter。它采用流水操作和穿通传输技术,可用于构成低延时、高带宽的高性能互连网络。这将种互连网络与上述结点系统结合,可以实现更大规模的并行系统。而且其无论在板级或是结点级都采用同一种互连技术,有利于系统的模块化实现。文中介绍了以存储器为中心的互连机制MCIM的原理,同时描述了以存储器为中心的路由器MRouter的结构及数据传输流程,在仿真实验中作者对MCIM的互连通信技术和其它互连技术进行了测试和比较。     

无线通讯基站设备中PCB之间的射频互连设计

文章介绍了无线通讯基站设备中PCB之间的各种射频互连设计,详细介绍了目前市场上比较普遍的从第一代到第三代PCB板对板,板到模块及共面板间的射频同轴连接器的设计、性能和应用,为无线通讯基站设备的板间互连设计提供比较详细的参考.     

CPS1432交换芯片的串行RapidIO互连技术

RapidIO技术是世界上第一个、也是目前唯一的嵌入式系统互连国际标准(ISO/IEC18372),串行RapidIO是针对高性能嵌入式系统芯片间和板间互连而设计的。本文介绍了CPS1432交换芯片与P2020组成的星型拓扑网络结构,包括硬件设计方案和软件设计要点,对高性能嵌入式互连设计具有很好的借鉴意义。     

XeMI板上高效可靠的数据传递机制

作为可扩展的并行计算机系统的关键部件之一，本文涉及的ＸｅＭＩ板沟通该系统的互连开关网络和作为结点的ＥＩＳＡ总线计算机系统，同时负责整个系统的软硬复位。它的高效和可靠的数据传递机制是系统至关重要的保障之一；它所采用的并行ＣＲＣ技术和互连接口技术是系统设计的典型技术；它所采用的ＥＩＳＡ接口技术是设计ＥＩＳＡ总线扩展板的关键。     

基于FPGA的10Gbps光互连方案设计与验证

针对光、电传输特点和高速互连网络的特殊要求,通过分析光互连技术的发展历史和研究现状,提出了10Gb/s光互连技术可行性的设计方案;并为了验证该方案的可行性,采用试验的方式,完成了基于10Gb/s光互连的PCB实验板设计.从实验板的设计可发现:Framer FPGA在传统电互连和光互连间提供相应的协议转换和速率转换起着关键作用.     

A systematic decision making approach for product conceptual design based on fuzzy morphological matrix

Conceptual design plays an important role in development of new products and redesign of existing products. Morphological matrix is a popular tool for conceptual design. Although the morphological-matrix based conceptual design approaches are effective for generation of conceptual schemes, quantitative evaluation to each of the function solution principle is seldom considered, thus leading to the difficulty to identify the optimal conceptual design by combining these function solution principles. In addition, the uncertainties due to the subjective evaluations from engineers and customers in early design stage are not considered in these morphological-matrix based conceptual design approaches. To solve these problems, a systematic decision making approach is developed in this research for product conceptual design based on fuzzy morphological matrix to quantitatively evaluate function solution principles using knowledge and preferences of engineers and customers with subjective uncertainties. In this research, the morphological matrix is quantified by associating the properties of function solution principles with the information of customer preferences and product failures. Customer preferences for different function solution principles are obtained from multiple customers using fuzzy pairwise comparison (FPC). The fuzzy customer preference degree of each solution principle is then calculated by fuzzy logarithmic least square method (FLLSM). In addition, the product failure data are used to improve product reliability through fuzzy failure mode effects analysis (FMEA). Unlike the traditional FMEA, the causality relationships among failure modes of solution principles are analyzed to use failure information more effectively through constructing a directed failure causality relationship diagram (DFCRD). A fuzzy multi-objective optimization model is also developed to solve the conceptual design problem. The effectiveness of this new approach is demonstrated using a real-world application for conceptual design of a horizontal directional drilling machine (HDDM).     

Antipatterns in the Creation of Intelligent Systems

A design pattern is a named problem-solution pair that enables large-scale reuse of software architectures or their components. Ideally, patterns explicitly capture expert knowledge, design trade-offs, and design rationale and make these lessons learned widely available for off-the-shelf use. They can also enhance developers' vocabulary - for example, by easing the transition to object-oriented programming. Conventionally, patterns consist of four elements: a name, the problem to be solved, the solution to the problem (often termed the refactored solution), and the consequences of the solution. Numerous sets of patterns (collectively known as pattern languages) exist for software design, analysis, management, and so on. Shortly after the notion of design patterns emerged, practitioners began discussing problem-solution pairs in which the solution did more harm than good. These have come to be known as antipatterns, and they are well known in the design and management communities     

Max-min surrogate-assisted evolutionary algorithm for robust design

Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variables or operating conditions, then it may not be appropriate to use this highly sensitive solution. In this paper, we focus on combining evolutionary algorithms with function approximation techniques for robust design. In particular, we investigate the application of robust genetic algorithms to problems with high dimensions. Subsequently, we present a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems. Empirical results are presented for synthetic test functions and aerodynamic shape design problems to demonstrate that the proposed algorithm converges to robust optimum designs on a limited computational budget.     

GLOBAL: A Design for Random Testability Algorithm

1MotivationsIncreasingdensityofVLSIchipsmakesthecostoftestingveryexpensive.Designfortestabilitycantemperthisproblemdrastically.Therearetwokindsofsolutions:(i)localsolutionl3'2]withlowcomputingcomplexity,whichcannotalwaysgetsatisfactoryresults;(ii)globa.lsolutionwithprohibitivecomputingcomplekityl1'5].T.H.Chen'andM.A.Breuer[1lpresentedanILPmodeltoplacetestpointsglobally.Becauseofitscomputingcomplealty,[1lcanonlydealwithsmallormediumscalecircuits.SCOAPisusedin[1],whichisnotaveryaccuratet…     

Fast convergence of inviscid fluid dynamic design problems

A new procedure for robust and efficient design optimization of inviscid flow problems has been developed and implemented on a wide variety of test problems. The methodology involves the use of an accurate flow solver to calculate the objective function and an approximate, dissipative flow solver, which is used only in the solution of the discrete quasi-time-dependent adjoint problem. The resulting design sensitivities are very robust even in the presence of noise or other non-smoothness associated with objective functions in many high-speed flow problems. The design problem is solved using what we term progressive optimization, whereby a sequence of a partially converged flow solution, followed by a partially converged adjoint solution followed by an optimization step is performed. This procedure is performed using a sequence of progressively finer grids for the solution of the flow field, while only using coarser grids for the adjoint equation solution.This approach has been tested on numerous inverse and direct (constrained) design problems involving two- and three-dimensional transonic nozzles and airfoils as well as supersonic blunt bodies. The methodology is shown to be robust and highly efficient, with a converged design optimization produced in no more than the amount of computational work to perform from 0.5 to 2.5 fine-mesh flow analyses.     

基于循环映射模型的概念设计自动化策略研究

建立了由主体特征和接口特征组成的功能特征模型,以及基于“需求域-功能域-原理解域循环映射”的概念设计模型;在此基础上提出了适用于计算机处理的概念设计与／或树形式化表达方法,探讨了相应的求解算法,开发了一个支持功能设计和功能求解的复合功能概念设计自动化原型系统,并以木材加工机械的概念设计为例进行了验证．     

Quantifying the impact of uncertainty in embedded systems mapping for NoC based architectures

We describe a modeling framework to capture and account for uncertainty in design parameters in embedded systems. We then develop an uncertainty-aware solution to the problem of mapping in embedded systems that uses Network-on-Chip (NoC) based architecture platforms. The problem of mapping is formulated as a multi-objective - reliability, performance, and energy consumption - optimization problem. To solve this problem, we propose a solution based on the NSGA-II genetic algorithm and Monte Carlo simulation techniques. The solution is implemented as a computer-aid design tool that can generate robust 3D Pareto frontiers in the solution space formed by the design objectives of reliability, performance, and energy consumption. Comparison to several state-of-the-art models and solutions for the mapping problem, indicate that significant differences in the actual values of the design attribute of interest exist when one considers uncertainty in design parameters. For example, in the case of mapping with reliability as the only objective, 10% uncertainty in design parameters can lead to a 10.06% difference in MTTF estimation. In the case of mapping with execution time and energy consumption as objectives, the difference in 2D Pareto frontiers due to 10% uncertainty in design parameters can be up to 7.9%. These differences are important because they can mislead the overall optimization process of mapping toward suboptimal solution points. The DESUU-NOC tool that implements the proposed multi-objective mapping algorithm has as a main feature and contribution of this paper the ability to generate 3D Pareto frontiers comprised of robust solution points.     

An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Tacit design knowledge plays an important role in the process of product design and is a valuable knowledge asset for enterprises. In terms of the characteristics of tacit rational design knowledge, this paper puts forward a scientific hypothesis and approach on capturing and reusing tacit rational design knowledge. The presented approach represents the observable design result facts of products using design knowledge graphs. A design issue-solving oriented knowledge graph model is presented, where directed relation edges represent design issues, and nodes stand for design solutions. When a new design solutions requirement needs to be searched, tacit design knowledge can be reused by relational learning for the constructed design knowledge graphs. In relational learning, the design knowledge graph is converted into a three-order tensor, where two modes are solution nodes, and the third mode holds the issue relations. Then, a tensor factorization approach is employed to calculate the latent features between design solutions for an issue relation. As a result, a score vector to represent the existence of issue-solution relations can be obtained. By sorting the scores in descending order, we may select the solution node with the highest score as the design solution to be searched. Finally, a stamping die design case study is provided. The case study shows that the proposed approach is feasible, and effective, and has better flexibility, scalability and efficiency than CBR methods.     

Parametric shape modification and application in a morphological biomimetic design

Biomimetic design performed to develop a solution-shape has been offered as a successful approach for overcoming the limitations of typical design methods. Especially for the nose-shape of high-speed trains, the morphological characteristics of a superior bio-model were used in the design process. However, current design methods using the biomimetic approach, particularly in the morphological domain, do not support a technique to evaluate how close the new solution is to the optimal one; nor do they support alternative methodologies used to validate and verify the solution-shape being developed. Solution optimization in a biomimetic design means not only preserving the original shape of a bio-model but also validating and verifying it. Shape optimization for a design problem should accompany shape evaluation and modification conducted according to criteria involving both evolutionary traits and technological constraints. In this research we suggest a method to verify the original shape, and to validate the solution, using theoretical backgrounds from both systematic biology and evolutionary biology. In this paper, the morphological characteristics of a bio-model are verified and modified using a quantitative method. To validate the solution developed, new criteria are applied for high-speed-train design.