面向片上网络路由器漏流功耗优化的自适应缓冲管理策略

随着单芯片上集成处理器数量的增加,片上网络逐渐成为多核处理器中非常有前景的互连结构.互连网络成为片上多处理器功耗的重要消耗部件之一.而输入缓冲器是路由器漏流功耗的最大消耗单元,采用门控电源是降低其漏流功耗的有效手段.自适应缓冲管理策略能够根据网络中通信量,自适应地关闭/打开缓冲的一部分,从而降低路由器漏流功耗.而为了减小对网络延迟的影响,该策略中采用的提前唤醒技术能够隐藏缓冲的唤醒延迟.在网络注入率较低情况下,两项缓冲不关闭策略下的网络延迟几乎不受唤醒延迟影响.模拟结果显示,在4×4的二维Mesh中,即使网络注入率为0.7,漏流功耗的节约率依然可以高达46％;网络注入率小于0.4时,两项缓冲不关闭策略下的网络延迟最大仅仅增加了3.8％.     

片上网络:一种新兴的片上系统设计方法

本文主要探讨了片上网络目前的发展状况和趋势,对比其他网络总结了片上网络的优缺点,并着重分析了片上网络的拓扑结构、通信方式。     

HHSR:一种命令与数据分传片上网络原型

在前面工作的基础上,根据大规模、超大规模片上网络互连结构的性能特点,针对网络所传输信息的不同特性以及对传输的不同要求,提出了一种命令与数据分传的片上网络原型系统HHSR。该原型系统分别在两套具有不同拓扑结构的片上网络中传输命令和数据,选取速度较快且综合性能较好的单环分级互连网络用于命令包的传输,以满足其实时性的要求,选取速度稍慢但成本较低的六边形Mesh网格用于数据包的传输。实验结果表明,这种命令与数据分传的片上网络原型系统在牺牲一定的数据包传送时间和花费一定成本的基础上,保证和提高了命令与控制信息的传送速度,从而保证和提高了整个片上多处理器的性能。     

片上互连网络的功耗特征分析与优化

随着处理器核数的增加,片上互连网络NoC结构日趋复杂,导致片上互连网络功耗所占的比重和功耗分析的难度也在增加。片上互连网络的任务映射,既要保证多处理器核心之间通信的高性能,又要保证耗费尽可能少的功耗和面积,即在有限的功耗和面积开销下获得较高的性能。在进行任务映射时,核心之间的通信距离是减少任务通信功耗的关键。连续且近凸的区域有助于缩短任务的通信距离。分析了一种功耗最优的片上互连网络启发式映射算法（INC）,该算法由区域选择算法和节点映射算法组成。对区域选择算法的2个因子进行了改进,使应用总的通信开销最小化且保证后续应用以很小的通信代价进行区域选择。提出了新的基于选择区域的映射算法。它们在动态到达程序映射问题中的实验结果表明,新的区域选择算法和节点映射算法相比于INC,可以减少12.10%的通信功耗,并且带来11.23%的通信延迟优化。     

片上网络互连拓扑综述

随着器件、工艺和应用技术的不断发展,片上多处理器已经成为主流技术,而且片上多处理器的规模越来越大、片内集成的处理器核数目越来越多,用于片内处理器核及其它部件之间互连的片上网络逐渐成为影响片上多处理器性能的瓶颈之一.片上网络的拓扑结构定义网络内部结点的物理布局和互连方法,决定和影响片上网络的成本、延迟、吞吐率、面积、容错...     

片上网络互连拓扑综述

随着器件、工艺和应用技术的不断发展,片上多处理器已经成为主流技术,而且片上多处理器的规模越来越大、片内集成的处理器核数目越来越多,用于片内处理器核及其它部件之间互连的片上网络逐渐成为影响片上多处理器性能的瓶颈之一。片上网络的拓扑结构定义网络内部结点的物理布局和互连方法,决定和影响片上网络的成本、延迟、吞吐率、面积、容错能力和功耗等,同时影响网络路由策略和网络芯片的布局布线方法,是片上网络研究中的关键之一。对比了不同片上网络的拓扑结构,分析了各种结构的性能,并对未来片上网络拓扑研究提出建议。     

同构与异构片上多核系统的演进过程

系统级芯片是高端电子系统的核心,而片上多核系统是近年来系统级芯片的主要实现形式。近十年来,片上多核系统一直是数字集成电路领域的热点,经过众多研究者的不断努力诞生了大量很有意义的研究成果。但由于片上多核系统的研究者背景和应用领域不同导致发展演进过程较为复杂而难以理解。为减少这一问题的影响,总结了片上多核系统的演进历史与现状,并对片上多核系统未来的发展提出了一些看法。     

分级环片上网络互连

在大规模、超大规模片上互连网络中,因为二维互连方式的性能较差而使多维互连方式成为可选方案之一.文中首先基于区域划分设计了一种分级环互连结构,分析了其静态互连特性,然后基于卡诺图编码设计了一种分级环互连的路由结构以及寻径方法,在均匀通信模式测试了不同的分级环级联链路缓冲区设置方法下网络的性能,详细分析了按照等比序列设置分级环级联链路缓冲区时分级环互连方式的动态网络特性,最后根据互连性能与Mesh等二维片上互连方式比较的结果,给出了分级环互连方式的使用场合.实验结果表明,虽然在较小规模网络中性能较差,但是分级环互连方式能以较低的成本、较高的性能实现大规模、超大规模片上网络的互连,其中单环分级互连方式在较低网络负载下综合性能更好,而双环分级互连方式则具有更大的网络负载能力,在较高网络负载下性能更好.     

多核片上系统的高效软硬件划分及调度算法

软硬件划分与调度是软硬件协同设计的关键环节,是经典的组合优化问题。本文针对调度与软硬件划分问题提出一种高效的启发式算法。调度算法根据任务的出度及软件计算时间对任务赋予不同的优先级,出度越大,优先级越高,出度相同的情况下,软件计算时间越大,优先级越高。划分算法首先寻找关键路径,然后将关键路径上具有最高受益面积比的任务交由硬件去实现。每次迭代更新当前关键路径的调度长度及剩余硬件面积。继续循环,直到剩余的硬件面积不再满足关键路径上的任何一个软件任务所需的硬件面积的要求为止,这样使得硬件面积的使用率比较高。实验表明,该算法对已有算法的改进可达到38%。     

片上多处理器互连技术综述

随着器件、工艺和应用技术的不断发展,片上处理器中处理器核的数目必将进一步增加,处理器芯片内部的互连及其通信成为影响处理器性能的重要因素.介绍了目前在片上多处理器中的几种典型互连方法,并简要分析了各种方法的优缺点.     

A software-hardware cosynthesis approach to digital systemsimulation

Our approach to digital system simulation compiles a high-level system model into a high-performance simulator that consists of software and hardware components. The target architecture for the simulation compiler is a tightly coupled processor and field-programmable gate array. We describe the simulation compiler and show how it can be used to improve simulation performance by up to a factor of two over an all-software simulator     

Context-aware enhancement of personalization services: A method of power optimization

Context-aware applications, which consist of a sensor system, a reasoning system and service artifacts such as mobile devices, kiosks and robots, require data from the sensors to be queried on a continuous basis. The smaller the sensing interval and the greater the amount of service time, the more accurate the service, but the more energy is consumed. Thus, use of context-aware applications always involves a trade-off. In this paper, we propose an automatic method of optimizing the level of personalization involving the sensing cycle and service time of a personalized application. The method proposes a quadratic form of total cost curve which demonstrated that the minimum identified value is always the global optimum. This eliminates the necessity of an exhaustive search for the minimum value for all levels of personalization.     

考虑功率预测的储能电站动态优化控制方法

提出了基于超短期功率预测的储能电站平抑风电功率波动的动态优化控制方法,系统考虑影响储能电站的多种因素,构建基于风功率超前信息的充放电策略;以荷电状态偏移量最小为目标构建优化控制模型,同时考虑储能介质的性能约束,得到下一次控制步长内的优化控制模式,由此形成递进式的动态控制策略。实际风电场运行数据算例分析表明,所提方法可有效实现储能电站的高效控制,具有一定实际应用价值。     

面向总线的低功耗优化方法探究

随着社会信息化水平的不断提高,信息产业的快速发展,由此带来了能源的消耗也越来越高。特别是芯片集成度越来越高,系统应用越来越复杂,这就使得功耗问题成为嵌入式系统必须面对的一个关键问题。单纯的硬件功耗优化已经不能满足要求,基于软件的功耗优化取得了很好的成效。在编译阶段,通过减少总线的翻转次数来降低系统的功耗。针对指令地址总线,结合遗传算法进行函数段的分配,结合相关的编码策略,减少总线翻转,从而降低其功耗。针对数据总线,使用蚁群算法进行指令调度,用0-1翻转编码,有效减少了其总线翻转,降低了功耗。这种基于数据总线和地址总线的优化算法,能够在特定的实验平台下通过实验验证,算法对于总线功耗的优化效率大约为25%左右。     

低功耗FPGA电子系统优化方法

首先与实测系统功耗进行对比,验证了Xilinx公司ISE软件包中FPGA功耗估算工具XPower的准确性。然后对FPGA设计中影响系统功耗的几个相互关联的参数进行取样,通过软件估算不同样点下的系统功耗,找到功耗最低的取样点,得到最佳设计参数,从而达到优化系统设计的目的。实验中通过这种方法,在一个FPGA读写SRAM的系统中,在单位时间读写操作数固定的条件下,选取了读写频率与读写时间占空比这两个参数来优化系统功耗。最终测试数据证明了该方法的正确性。     

考虑发电能力匹配性的输变电设备检修优化方法

为保障电网电力供应能力,减少由于发电机组与输变电设备检修计划协调性不足导致的发电受限问题,提出了一种考虑发电能力匹配性的输变电设备检修优化方法。充分考虑发电机组检修及季节性能力变化与输变电设备检修计划间的协调关系,定义了发电能力匹配性指标。该指标取值大小反映了输变电设备检修与发电机组发电能力的匹配关系,数值越大,匹配性越高。以该指标最大化为优化目标,综合考虑输变电设备检修时段、电力电量平衡等约束条件,构建了考虑发电能力匹配性的输变电设备检修优化模型。根据模型特点,提出了求解方法。最后基于IEEE-30节点系统构造算例表明,本文所提出的方法能够避免发电机组检修与输变电设备检修不协调导致的发电能力下降,从而提升电网整体运行效益。     

A potential-based clustering method with hierarchical optimization

This work proposes a novel data clustering algorithm based on the potential field model, with a hierarchical optimization mechanism on the algorithm. There are two stages in this algorithm. Firstly, we build an edge-weighted tree based on the mutual distances between all data points and their hypothetical potential values derived from the data distribution. Using the tree structure, the dataset can be divided into an appropriate number of initial sub-clusters, with the cluster centers close to the local minima of the potential field. Then the sub-clusters are further merged according to the well-designed merging criteria by analyzing their border potential values and the cluster average potential values. The proposed clustering algorithm follows a hierarchical clustering mechanism, and aims to optimize the initial sub-cluster results in the first stage. The algorithm takes advantage of the cluster merging criteria to merge the sub-clusters, so it can automatically stop the clustering process without designating the number of clusters in advance. The experimental results show that the proposed algorithm produces the most satisfactory clustering results in most cases compared with other existing methods, and can effectively identify the data clusters with arbitrary shape, size and density.     

A heuristic method for simultaneous tower and pattern-free field optimization on solar power systems

A heuristic method for optimizing a solar power tower system is proposed, in which both heliostat field (heliostat locations and number) and the tower (tower height and receiver size) are simultaneously considered.Maximizing the thermal energy collected per unit cost leads to a difficult optimization problem due to its characteristics: it has a nonconvex black-box objective function with computationally expensive evaluation and nonconvex constraints.The proposed method sequentially optimizes the field layout for a given tower configuration and then, the tower design is optimized for the previously obtained field layout. A greedy-based heuristic algorithm is presented to address the heliostat location problem. This algorithm follows a pattern-free method. The only constraints to be considered are the field region and the nonconvex constraints (which allow heliostats to not collide).The absence of a geometrical pattern to design the field and the simultaneous optimization of the field and the tower designs make this approach different from the existing ones. Our method is compared against other proposals in the literature of heliostat field optimization.     

Energy-efficient NoC with multi-granularity power optimization

As the core count grows rapidly, NoC (Network-on-Chip) consumes an increasing fraction of the modern processors/SoCs (System-on-Chips) power. It is thus very important to design energy-efficient NoC architecture. Multi-NoC (Multiple Network-on-Chip) has demonstrated its advantages in power gating for reducing leakage power, which constitutes a significant fraction of NoC power. In this paper, we propose Chameleon, a novel heterogeneous Multi-NoC design. Chameleon employs a fine-grained power gating algorithm which exploits power saving opportunities at different levels of granularity simultaneously. Integrated with a congestion-aware traffic allocation policy, Chameleon is able to achieve both high performance and low power at varying network utilization. Our experimental results on both synthetic and real workloads show that Chameleon delivers an average of 2.61 % higher performance than Catnap, the best in the literature. More importantly, Chameleon consumes an average of 27.75 % less power than Catnap.     

A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation

The reactive power optimization is an effective method to improve voltage level, decrease network losses and maintain the power system running under normal conditions. This paper provides a method combining particle swarm optimization (PSO) with linear interior point to handle the problems remaining in the traditional arithmetic of time-consuming convergence and demanding initial values. Furthermore, since chaotic mapping enjoys certainty, ergodicity and stochastic property, the paper introduces chaos mapping into the particle swarm optimization, the paper presents a new arithmetic based on a hybrid method of chaotic particle swarm optimization and linear interior point. Thanks to the superior overall exploration ability of particle swarm optimization and the local exploration ability of linear interior point within the neighborhood of the optimal point, the new method can improve the performance of both convergence and results’ precision. Tested by IEEE-30, the new method provided in this paper is proved effective and practical in the optimization of shunt capacitors and tap position of load-ratio voltage transformer.