一种基于电压岛的NoC低能耗路由算法

针对基于电压频率岛的片上网络路由算法通信能耗高的问题,提出一种确定性路由算法.应用遗传算法综合考虑电压岛的电压和频率对能耗与延迟的影响,在满足时延约束的条件下得到能耗较低的确定性路由路径,降低通信能耗.通过对遗传算法罚函数的改进,减少通信热点.实验结果表明,与已有算法相比,该算法以少量的硬件开销为代价,可得到通信能耗和通信热点两方面的优化.     

改进的三级遗传算法定制特定应用片上网络拓扑

特定应用片上网络的拓扑优化是一种NP难的问题,吸引着很多研究人员对其进行研究,G.Leary等在VLSI刊物上提出的三级遗传算法进行拓扑制定,但其存在遗传个体数量大,仿真时间长的缺点。针对G.Leary的三级遗传算法的遗传个体数量巨大,仿真速度慢的不足,提出一种改进的三级遗传算法来进行拓扑优化定制。实验结果表明,我们的改进算法虽然在能耗方面改进不大,仅有3.74%,但是仿真时间却有较大的减少,由309.125秒减少至254.2秒,平均提高17.4%,因此我们的GA算法具有较好的实用价值。     

基于遗传算法的特定应用片上网络映射研究

片上网络是片上系统SoC通信问题的一种最有效解决方法,如何把知识产权核映射到网格之格件映射问题是NoC设计的关键问题之一。映射问题本质上是一种二次分配的NP难问题,遗传算法能够有效地求解问题的近似最优解。提出一种基于遗传的IP映射算法,实验结果表明,遗传算法能够在几分钟内求得最小能耗的映射。     

面向测试优化的片上网络映射算法

针对复杂片上系统(SoC)芯片的片上网络(NoC）映射方案未考虑测试需求的问题,提出了一种面向测试优化的NoC映射算法,兼顾了可测性的提升和映射开销的最小化。该映射方案首先依据特定的测试结构,使用划分算法进行片上系统所有IP核的测试分组,其优化目标为测试时间最短;之后,再基于分组内IP核之间的通信量,应用遗传算法实现NoC映射,其优化目标是在测试优化的基础上实现映射开销最小。通过多个ITC'02测试基准电路进行的实验结果表明:应用该方案后,测试时间平均减少12.67%;与随机任务映射相比,映射代价平均减少24.5%。     

一种基于SLP的异构NoC布图优化算法

随着超大规模集成电路的飞速发展,越来越多的IP核被集成到片上网络(Network-on-Chip,NoC)上,而片上网络是一种以通信为中心的片上互连结构,与传统总线型片上系统(System on Chip,SoC)相比具有低功耗、高宽带的显著优势。笔者将工业工程中系统布局设计(Systematic Layout Planning,SLP)方法与遗传算法相结合,提出一种系统布图优化算法。相对于传统的布图算法,提出的系统布图优化算法不仅具有更优的搜索能力,而且可定性与定量地优化布图方案,为片上网络布图规划提供了一种全新思路。实验结果表明,随着IP核之间平均通信量的增加功耗降低幅度逐渐减小,当IP核之间的平均核通信量在1 000 Mb/s以内时,总功耗平均降低了40.51%。     

基于遗传算法的能量均衡覆盖控制策略

研究优化网络通信、延长网络寿命问题,由于无线传感器网络中覆盖率、工作节点数和能耗均衡互相矛盾。为了选择最优覆盖节点集基础上,同时考虑网络区域能耗的均衡特点,提出一种遗传算法的能量均衡覆盖控制策略。构建概率感知模型网络,定义一个能耗均衡系数用以表示网络能耗均衡程度,以覆盖率、工作节点数和网络能耗均衡系数为优化目标,然后利用遗传算法进行仿真。仿真结果表明,覆盖控制策略能够在达到较高覆盖率的同时,有效降低能耗并保证网络能量均衡,从而延长网络生存时间。     

基于通信链路的NoC映射算法

片上网络映射算法对系统通信能耗、延时等性能具有重大影响。基于通信链路通信量大小,提出一种改进的多目标遗传映射算法,以降低系统能耗和延时。算法中提出根据通信链路通信量大小决定任务在网络拓扑中映射位置的方式来产生初始染色体,并通过改进的变异操作产生新的子代,有效降低了算法复杂度,加快了算法的收敛速度。实验通过NIRGAM仿真平台进行,结果表明,与传统的多目标遗传算法相比,在实际应用DVOPD中,能耗降低了49.76%,通信延时降低了53.23%;在VOPD实验中,能耗和延时分别降低了29.54%和32.45%;而MPEG-4的能耗和延时则分别降低了45.72%和49.40%。同样,提出的算法与模拟退火算法相比,能耗和延时性能也有明显提高。     

特定应用片上网络的研究综述

特大规模集成电路技术的飞速发展,使得把大量的知识产权（Intellectual Property,IP）核集成到单一的芯片上形成的片上系统成为了今后微电子发展的主流趋势。片上系统面临着许多设计和制造问题,片上网络为解决片上系统的这些问题提供一种行之有效的方案。当前及今后的片上系统都主要面向特定应用或特定应用类,因此,片上网络也是面向特定应用的片上网络,对特定应用片上系统面临的问题、特定片上网络的提出、发展、和主要研究内容进行综述。     

面向温度优化的片上网络任务调度方法

随着片上网络规模的扩大和研究的逐步深入,如何将芯片上众多的任务进行合理的调度成为系统温度优化的关键之一。针对片上网络任务调度问题, 提出一种基于最短曼哈顿距离的任务调度SMDS方案。该策略充分考虑核通信图中通信节点对之间最短曼哈顿路径,通过搜索算法寻找任务调度的目的节点,使用模拟退火算法确定任务调度对。实验结果显示,与传统的分布式任务调度 DTM策略相比,针对6*6、8*8和10*10的拓扑结构,SMDS实验方案在迁移次数方面的平均优化率分别为2208%、21.74%和23.02%。在平均跳数方面的平均优化率分别为24.04%、29.18%和23.04%,实现了系统温度优化。     

基于程序访存行为的片上网络能耗优化方法

片上网络作为未来多核处理器片上互连的发展趋势,其能耗优化问题越来越受到重视。动态电压频率调节是一种有效的运行时能耗管理手段,近年来已被许多研究者用在片上网络的能耗优化上。针对目前主流的分布式控制方案存在软硬件代价高的缺点,提出了一种全局控制的能耗优化方法,通过监控程序的关键性访存信息来动态调整片上网络的电压和频率。仿真结果表明,在允许性能损失5%的限制下,该方法可以实现约45%的能耗节省。     

A heuristic clustering approach to use case-aware application-specific network-on-chip synthesis

Network-on-chip (NoC) is a promising paradigm for efficient communication between the processing elements inside multi-core system-on-chip (SoC) and general purpose chip-multi-processor. Choosing appropriate topology for NoCs with predefined application characteristics plays a pivotal role in improving power and area metrics. Until now, different irregular topologies with varying objective optimization parameters have been offered. In this paper, a novel heuristic topology synthesis method for creating application-specific NoCs consisting of some use cases which are described the applications characteristics has been proposed. This approach is composed of application clustering for assigning cores to specific routers, topology construction for finding a routing path for all flows, and also link insertion for producing final topology by interconnecting the routers. To confirm the proposed method, results of an industrial smartphone SoC and some generic benchmarks have been used as case studies. Experimental results demonstrate the benefits of the proposed method compared to state-of-the-art approaches.     

认知智能电网中基于能效优化的频谱分配策略

针对智能电网的无线通信环境存在频谱短缺、资源利用效率低等问题,将认知无线电技术应用于智能电网的邻域网络通信中.引入认知智能电网概念以保证业务传输的公平性和有效性,提出一种基于改进二进制蝴蝶优化算法(BOA)的频谱分配策略,此方案考虑了通信过程中的信噪比和路径损耗,选择系统能量效率作为信道效益,并且在拓扑结构固定的城市居民小区进行建模仿真.首先,使用基于改进时变转换函数和扰动策略的二进制蝴蝶优化算法(IBBOA)为认知智能电网用户进行频谱分配;然后,采用基于接收信噪比的闭环功率控制算法动态调整用户的传输功率,减少认知智能电网用户和主要用户之间存在的干扰;最后,以系统能量效率和两个用户公平性指数为优化目标,与遗传算法(GA)和二进制粒子群算法(BPSO)进行对比实验.仿真实验表明,联合闭环功率控制的IBBOA算法所获得的系统能量效率比未联合闭环功率控制的NBOA算法高33.2%,IBBOA算法最终的系统能量效率和用户公平性指数fair比GA算法分别高出47.8%和62.6%,比BBOA算法分别高出17.6%和26.7%.结果表明所提方案能够有效抑制认知智能电网中用户间的干扰,大大提高频谱利用率和系统能量效率.     

Crossbar总线与共享总线相结合的SoC系统级通信综合方法研究

提出了一种Crossbar总线与共享总线相结合的SoC系统级通信综合方法.从实际应用的系统级设计出发,根据待互连处理单元和存储单元之间的通信量,综合出Crossbar总线与共享总线相结合的总线拓扑结构.采用遗传算法,以实际应用的通信延迟为约束,考虑总线竞争、通信同步带来的通信延迟,综合出满足延迟约束的总线参数.对综合后的Crossbar总线与共享总线进行事务级建模和分析,进一步优化生成的总线拓扑结构.实验证明,该方法解决的问题较以往更加全面,生成的总线拓扑结构和参数更优.     

Topology mapping of irregular parallel applications on torus-connected supercomputers

Supercomputers with ever increasing computing power are being built for scientific applications. As the system size scales up, so does the size of interconnect network. As a result, communication in supercomputers becomes increasingly expensive due to the long distance between nodes and network contention. Topology mapping, which maps parallel application processes onto compute nodes by considering network topology and application communication pattern, is an essential technique for communication optimization. In this paper, we study the topology mapping problem for torus-connected supercomputers, and present an analytical topology mapping algorithm for parallel applications with irregular communication patterns. We consider our problem as a discrete optimization problem in the geometric domain of a torus topology, and design an analytical mapping algorithm, which uses numerical solvers to compute the mapping. Experimental results show that our algorithm provides high-quality mappings on 3-dimensional torus, which significantly reduce the communication time by up to 72%.     

延迟优化的片上网络低功耗映射*

片上网络（NoC）是解决传统基于总线的片上系统（SoC）所面临的功耗、延迟、同步和信号完整性等挑战的有效解决方案。功耗和延迟是NoC设计中的重要约束和性能指标,在设计的各个阶段都存在着优化空间。基于蚁群优化算法,通过通信链路上并发通信事件的均匀分布来降低NoC映射阶段的功耗和延迟。仿真实验表明,与链路通信量负载均衡的方法相比,该方案能进一步在拓扑映射阶段优化功耗和延迟。     

机载PHM中的无线传感器网络功率控制算法

由于机载环境的复杂性,机载故障预测与健康管理(PHM)系统采用无线传感器网络(WSNs)技术进行数据采集。鉴于机载PHM对消息传输高实时性的要求,需要通过功率控制来优化网络拓扑,减少网络平均长度。提出一种基于小世界理论的功率控制算法(PCS),该算法通过添加捷径来降低网络平均路径长度,并采用遗传算法对捷径进行优化,得到通信代价较小、网络平均路径长度较短的捷径。仿真结果表明:PCS算法优化了网络拓扑,缩短了网络平均路径长度,提高了信息传输速率,并且在较大的传感器网络环境下也具有较好的适用性。     

Efficient Single-Stage Bridgeless AC to DC Converter Using Grey Wolf Optimization

Bridgeless single-stage converters are used for efficient (alternative current) AC-(direct current) DC conversion. These converters control generators, like electromagnetic meso- and micro-scale generators with low voltage. Power factor correction helps increase the factor of the power supply. The main advantage of the power factor is it shapes the input current for increasing the real power of the AC supply. In this paper, a two-switch bridgeless rectifier topology is designed with a power factor correction capability. For the proposed converter topology to have good power quality parameters, the closed loop scheme, which uses the grey wolf optimization (GWO) algorithm, is implemented. The successes of GWO encourage this research to implement GWO in the topology. The performance of the proposed topology is analyzed under different load conditions. Simulation is carried out using the MATLAB/Simulink environment, and the results are compared with those of conventional (proportional integral derivative) PID and (particle swarm optimization) PSO controllers. To validate the simulation results, a 350-W hardware prototype is implemented, and the voltage ripple, efficiency, and power factor under different load conditions are analyzed and tabulated. The comparative study clearly indicates that the proposed converter topology with a closed loop control scheme using the GWO algorithm improves the power factor to 0.9732 and reduces the voltage ripple to 0.12% with a conversion efficiency of 98.25%.     

Performance-driven assignment and mapping for reliable networks-on-chips

Network-on-chip （NoC） communication architectures present promising solutions for scalable communication requests in large system-on-chip （SoC） designs. Intellectual property （IP） core assignment and mapping are two key steps in NoC design, significantly affecting the quality of NoC systems. Both are NP-hard problems, so it is necessary to apply intelligent algorithms. In this paper, we propose improved intelligent algorithms for NoC assignment and mapping to overcome the draw-backs of traditional intelligent algorithms. The aim of our proposed algorithms is to minimize power consumption, time, area, and load balance. This work involves multiple conflicting objectives, so we combine multiple objective optimization with intelligent algorithms. In addition, we design a fault-tolerant routing algorithm and take account of reliability using comprehensive performance indices. The proposed algorithms were implemented on embedded system synthesis benchmarks suite （E3S）. Experimental results show the improved algorithms achieve good performance in NoC designs, with high reliability.     

Design of Clustering Techniques in Cognitive Radio Sensor Networks

In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random Secondary Users (SUs), and PUs are considered for implementation. Hence, the proposed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algorithms. Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646% compared to the existing algorithms. Similarly, ASDIC algorithm reduced 24.23% of SUs average node power compared to the existing algorithms. Probability of detection is higher by reducing the Signal-to-Noise Ratio (SNR) to 2 dB values. The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection. Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of network capacity.     

基于通信量权值二分图分配算法的列车通信网络研究

针对列车通信网络的网络性能直接受到网络拓扑结构的影响,提出一种基于设备间的通信量权值的二分图分配算法,解决网络拓扑中设备到交换机的分配问题。首先,根据列车通信网络中各设备间的实际通信情况,建立列车交换式以太网模型,得出设备间的通信量权值;然后,利用通信量权值的二分图分配算法完成设备到交换机的分配,构建新的交换式列车网络拓扑结构。通过OPNET建模仿真对该结构的网络性能进行分析,结果表明,优化后的列车通信网络拓扑结构比未优化的拓扑结构,在网络时延、链路利用率和吞吐量等网络性方面能均有很大的提升,可为列车通信网络拓扑结构优化研究提供理论参考。