网络处理器技术综述

本文在归纳总结网络处理器所涉及的关键技术基础之上，介绍了网络处理器的主要体系结构及其在网络设备中的主要作用，并针对未来网络建设和网络设备发展的需要，提出了网络处理器可能的发展趋势。     

网络处理器及其应用

本首先介绍了网络处理的基本概念,然后介绍基于网络处理器 斩基本架构,最后介绍网络处理器的应用。     

基于IXP2400网络处理器的DiffServ系统模型

网络处理器是新一代用采执行数据处理和转发的高速可编程处理器，它代表了未来网络设备设计的发展方向。本文主要介绍了Intel公司推出的网络处理器IXP2400的功能结构，同时详细的介绍了基于IXP2400的DiffServ的实现模型。为以后基于网络处理器的应用开发提供一些借鉴经验。     

ZiiLABS推出全球首款1080p蓝光品质掌上型应用媒体处理器ZMS-08

ZiiLABS近日发布第三代富媒体应用处理器ZMS-08，该处理器采用H．264解码技术，为低功耗设备带来71080p蓝光品质。ZMS—08将ZiiLABS干细胞计算阵列灵活的多格式媒体处理能力与1GHz的ARMCortex处理器结合在一起，使其性能达到了上一代器件的4倍以上，并提供了下一代网络上设备所要求的低功耗、高性能的富媒体处理能力。这些设备包括网络本、上网本、互联网电视、视频会议和家庭媒体中心等。     

基于网络处理器的路由器体系结构

目前许多半导体厂商开始销售一种称为网络处理器的芯片。网络处理器和通用微处理器很相似，但在报文处理能力方面作了优化，从而特点适合于网络通信设备。文章从路由器的体系结构出发，对传统通用处理器和网络处理器实现报文处理的方法进行了比较，最后对网络处理器转发引擎作了详细的分析。     

多核处理器悄然崛起

本文介绍了双核及多核处理器的发展动态，包括多核处理器的优点、多核MPU、双核MCU、四核DSP和双核网络处理器。还介绍了当前多核处理器所存在的问题。     

下一代网络的核心技术——网络处理器

网络应用的飞速发展和对线速的智能化处理的需求导致了网络处理器的出现。可编程的网络处理器不但提供了以线速处理数据包的高性能的硬件功能,同时还具备极大的系统灵活性。虽然当前的网络处理器的结构设计各不相同,随着网络技术的发展,网络处理器必将成为下一代网络的核心技术。结合当前的网络发展趋势,从现有的网络处理器的结构分析入手,论述了满足下一代网络应用需求的网络处理器应该具有的结构特点。     

基于INTEL网络处理器的系列硬件平台-FIDS

本文从网络处理器的基本概念入手,对INTEL网络处理器的结构进行了描述,比较了网络处理器技术与Intel X86、ASIC两种技术的区别,分析了网络处理器在网络应用方面的优势,介绍了与INTEL网络处理器同步发展的FIDS42x,FIDS12xx,FIDS24xx 的结构特点和应用.     

网络处理器的分析与演进

文章从网络处理器的出现首先分析了网络处理器具备的基本功能特性,接着介绍了网络处理器体系结构的演进过程,在文章的最后部分对当前市场上的主流产品作了一个大概的介绍,并对网络处理器今后的发展作了简单的描述.     

网络处理器设计分析及其应用前景

当今网络容量和性能的高速发展迫切要求网络设备具有线速和智能的处理能力。当传统的设计方案难以同时满足这两方面的需求时,网络处理器应运而生。本文详细介绍了网络处理器技术,根据网络设备开发需求,分析了网络处理器软硬件设计要求。在简要介绍了当今市场上几种主流产品基础上,对网络处理器所面临的挑战及今后的发展作了简短的讨论。     

C4NP-无铅倒装晶片焊凸形成生产工艺与可靠性数据

受控倒塌芯片连接新工艺是一种由IBM公司开发、由Suss Micro Tec公司推向商品化的新型焊凸形成技术。受控倒塌芯片连接新工艺采用各种无铅焊料合金致力于解决现有的凸台。形成技术限定,使低成本小节距焊凸形成成为可能。受控倒塌芯片连接新工艺是一种焊球转移技术,熔焊料被注入预先制成并可重复使用的玻璃模板(模具)。这种注满焊料的模具在焊料转入圆片之前先经过检查以确保高成品率。注满焊料的模具与圆片达到精确的接近后以与液态熔剂复杂性无关的简单工序转移在整个300mm(或300mm以下)圆片上。受控倒塌芯片连接新工艺技术能够在焊膏印刷中实现小节距凸台形成的同时提供相同合金选择的适应性。这种简单的受控倒塌芯片连接新工艺使低成本、高成品率以及快速封装周期的解决方法对于细节距FCiP以及WLCSP凸台形成均能适用。     

On some unresolved issues in finding optimum distributed detection schemes

Optimum distributed detection under the Neyman-Pearson (NP) criterion is considered for a general case with possibly dependent observations from sensor to sensor. The focus is on the parallel architecture. New necessary conditions are presented that relate the threshold used in the NP-optimum fusion rule to those used in the NP-optimum sensor rules. These results clearly illustrate that the necessary conditions for NP optimality have exactly the same form as those for Bayes optimality. Based on these conditions, a new algorithm for finding NP optimum distributed detection schemes is developed. The algorithm allows randomization at the fusion center, which we show is generally needed to achieve optimality. The algorithm allows one to attempt to optimize the fusion rule along with the sensor rules or to find the best schemes among those using each of a set of fixed possible fusion rules.     

Creating advanced functions on network processors: experience and perspectives

In this article we present five case studies of advanced networking functions that detail how a network processor (NP) can provide high performance and also the necessary flexibility compared with ASIC. We first review the basic NP system architectures, and describe the IBM PowerNP architecture from the data plane as well as the control plane point of view. We introduce models for the programmer's views of NP that facilitate a global understanding of NP software programming. Then, for each case study, we present results from prototypes as well as general considerations that apply to a wider range of system architectures. Specifically, we investigate the suitability of NP for QoS (active queue management and traffic engineering), header processing (GPRS tunneling protocol), intelligent forwarding (load balancing without flow disruption), payload processing (code interpretation and just-in-time compilation in active networks), and protocol stack termination (SCTP). Finally, we summarize the key features as revealed by each case study, and conclude with remarks on the future of NP.     

Recent Development of Active Nanoparticle Catalysts for Fuel Cell Reactions

This review focuses on the recent advances in the synthesis of nanoparticle (NP) catalysts of Pt‐, Pd‐ and Au‐based NPs as well as composite NPs. First, new developments in the synthesis of single‐component Pt, Pd and Au NPs are summarized. Then the chemistry used to make alloy and composite NP catalysts aiming to enhance their activity and durability for fuel cell reactions is outlined. The review next introduces the exciting new research push in developing CoN/C and FeN/C as non‐Pt catalysts. Examples of size‐, shape‐ and composition‐dependent catalyses for oxygen reduction at cathode and formic acid oxidation at anode are highlighted to illustrate the potentials of the newly developed NP catalysts for fuel cell applications.     

Hardness of approximating the minimum distance of a linear code

We show that the minimum distance d of a linear code is not approximable to within any constant factor in random polynomial time (RP), unless nondeterministic polynomial time (NP) equals RP. We also show that the minimum distance is not approximable to within an additive error that is linear in the block length n of the code. Under the stronger assumption that NP is not contained in random quasi-polynomial time (RQP), we show that the minimum distance is not approximable to within the factor 2/sup log1-/spl epsi//(n), for any /spl epsi/>0. Our results hold for codes over any finite field, including binary codes. In the process, we show that it is hard to find approximately nearest codewords even if the number of errors exceeds the unique decoding radius d/2 by only an arbitrarily small fraction /spl epsi/d. We also prove the hardness of the nearest codeword problem for asymptotically good codes, provided the number of errors exceeds (2/3)d. Our results for the minimum distance problem strengthen (though using stronger assumptions) a previous result of Vardy (1997) who showed that the minimum distance cannot be computed exactly in deterministic polynomial time (P), unless P = NP. Our results are obtained by adapting proofs of analogous results for integer lattices due to Ajtai (1998) and Micciancio (see SIAM J. Computing, vol.30, no.6, p.2008-2035, 2001). A critical component in the adaptation is our use of linear codes that perform better than random (linear) codes.     

Efficient NP Tests for Anomaly Detection Over Birth-Death Type DTMCs

We propose computationally highly efficient Neyman-Pearson (NP) tests for anomaly detection over birth-death type discrete time Markov chains. Instead of relying on extensive Monte Carlo simulations (as in the case of the baseline NP), we directly approximate the log-likelihood density to match the desired false alarm rate; and therefore obtain our efficient implementations. The proposed algorithms are appropriate for processing large scale data in online applications with real time false alarm rate controllability. Since we do not require parameter tuning, our algorithms are also adaptive to non-stationarity in the data source. In our experiments, the proposed tests demonstrate superior detection power compared to the baseline NP while nearly achieving the desired rates with negligible computational resources.     

A Neyman-Pearson approach to statistical learning

The Neyman-Pearson (NP) approach to hypothesis testing is useful in situations where different types of error have different consequences or a priori probabilities are unknown. For any /spl alpha/>0, the NP lemma specifies the most powerful test of size /spl alpha/, but assumes the distributions for each hypothesis are known or (in some cases) the likelihood ratio is monotonic in an unknown parameter. This paper investigates an extension of NP theory to situations in which one has no knowledge of the underlying distributions except for a collection of independent and identically distributed (i.i.d.) training examples from each hypothesis. Building on a "fundamental lemma" of Cannon et al., we demonstrate that several concepts from statistical learning theory have counterparts in the NP context. Specifically, we consider constrained versions of empirical risk minimization (NP-ERM) and structural risk minimization (NP-SRM), and prove performance guarantees for both. General conditions are given under which NP-SRM leads to strong universal consistency. We also apply NP-SRM to (dyadic) decision trees to derive rates of convergence. Finally, we present explicit algorithms to implement NP-SRM for histograms and dyadic decision trees.     

REFINE: The reconfigurable packet filtering on network processor

Network processors (NPs) are emerging as very promising platforms for developing reconfigurable and high‐performance network devices, due to their capability to combine the flexibility of general‐purpose processors with the high‐performance features of hardware‐based systems. They represent the most suitable solutions for implementing complex and dynamic tasks, such as packet classification and scheduling, which are key operations, for example, in DS networks. Programmability and reconfigurability allow NP‐based devices to be continuously adapted to the new network requirements, obtaining a high time in market. This paper illustrates the compound process that leads to the implementation of a reconfigurable multidimensional packet filtering on the Intel^® IXP2400 NP. The multidimensional multibit trie is chosen as the best algorithm to be implemented and it is modified to exploit the specific features of NP. The different tasks are mapped on the NP computational resources and an optimized implementation is performed, with subsequent experimental validation. Copyright © 2008 John Wiley & Sons, Ltd.     

Room Temperature Electric‐Field Control of Magnetism in Layered Oxides with Cation Order

Searching for materials with room‐temperature electric‐field control of magnetism has interested researchers for many years with three‐dimensional perovskite BiFeO₃‐based compounds as the main focus. Here we choose the layered hybrid improper ferroelectric Ruddlesden‐Popper oxides as a platform from which to realize electric field controllable magnetism, leveraging a recently identified strain tunable polar‐to‐nonpolar (P‐NP) transition. We first propose a design principle for selecting the required A and B cation chemistries that will ensure (001) A₃B₂O₇ films exhibit P‐NP transitions, which we substantiate with density functional calculations. By extending the guideline to B‐site ordered A₃BB′O₇ oxides, we identify more compounds exhibiting P‐NP transitions marked by the disappearance of an in‐plane polarization that can be functionalized. We then demonstrate that weak ferromagnetism can be tuned by an electric field at the boundary of the P‐NP transition in B‐site ordered (001) A₃BB′O₇ magnetic films, based on which we predict that cation ordered Ca₃TcTiO₇ may be a viable candidate for room‐temperature electric‐field control of magnetism.