一种基于可测性的寄存器分配算法

提出了一种在高层次综合的寄存器分配过程中考虑可测性的算法，该算法在将一个调度好的CDFG（Control Data Flow Graph)的变量分配到相应的寄存器的过程中，通过对未能分配复用到输入、输出寄存器的变量进行可测性处理，达到提高设计可测性的目的，同时在进行可测性处理的时候，定义了CDFG的节点的可测性测度方法。     

高层次综合中的可测性模块分配方案

高层次综合中的模块分配会直接影响到寄存器分配方案,进而影响到综合后电路的面积、时延、功耗和可测性.为此提出一种面向可测性的模块分配方案.在讨论了考虑可测性的模块分配原则之后,提出面向可测性的模块分配的权重图模型,并在此基础上给出基于权重图的可测性模块分配算法.按照最大可测性提高的原则,通过动态地修改权重对模块进行考虑可测性的均衡分配,并最终输出模块分配方案.对标准电路进行实验的结果表明,除了较小的面积开销外,采用文中方案的电路的可测性优于其他方案.     

一种基于多目标遗传算法的高层次测试综合方法

提出一种基于多目标遗传算法的高层次测试综合方法。该方法在面积和时间的约束下,在高层次调度和单元分配过程中完成电路的可测性设计。通过约束条件的转换和新式的杂交算子来避免无效染色体的产生,改善了遗传算法中解的质量和收敛速度。实验结果表明了该方法的有效性。     

面向控制流密集型电路的高层次低功耗方法*

通过对控制流密集型电路的分析和研究,提出一种面向控制流密集型电路的高层次低功耗综合方法。在调度过程中,应用分支控制和多电压两种方法对设计电路功耗进行双重优化。实验结果表明,在相同的时间和资源约束下,该方法比单独应用分支控制的调度方法功耗平均降低19.04%,比单独应用多电压的调度方法功耗平均降低5.74%。     

应用拉格朗日乘数法的多电压低功耗调度方法

提出了一种在时间和资源约束下的高层次多电压低功耗调度方法。应用拉格朗日乘数法,将空闲时间分布到数据通路的各个操作节点上,从而有效地降低了设计电路的能耗。该方法在调度和调整过程中,可以同时处理关键通路和非关键通路上的节点。实验结果表明了该方法在功耗优化方面的有效性。     

一种基于遗传算法的高层次测试综合方法

提出一种基于遗传算法的高层次测试综合方法,在进行各种资源分配的过程中同时考虑可测性问题。该方法主要的特点在于提出了一种新颖的染色体编码方法,并设计了相应的遗传操作,避免了在进化过程中不可行解的产生。实验结果表明了算法在可测性方面的有效性。     

基于扫描的低测试功耗结构设计

在集成电路设计中,面积、功耗和可测性是3个最为重要的优化指标,测试成本正随着集成电路规模的不断增大而提高,因此在设计中加入可测性设计的考虑已成为共识,基于扫描的可测性设计方法是目前应用最广泛的方法之一,加入扫描结构可以大大提高电路系统的测试性能,但同时也会给系统的面积、性能、功耗等带来一些负面影响,提出一种考虑低功耗因素的可测性设计方法,计算数据显示,与传统扫描设计方法相比,这种方法在改善系统测试功耗方面具有突出的优势。     

基于RTL内部功能模块的测试向量产生方法

提出利用RTL数据通路中加法器、减法器、乘法器作为一种产生测试向量硬件的方法,对被测模块进行测试,以降低硬件开销。实验结果表明,该可测性设计与调度算法具备良好的性能和实用性。在满足功耗约束下,硬件开销可以降低10.7%~73.4%,同时测试应用时间也有所降低,最多可以降低22.4%。     

高层次综合中基于整数线性规划模型的多目标功耗优化算法

考虑峰值周期功耗和峰值模块功耗的同时优化,并尽可能地降低电路的功耗时延乘积指标.利用参数化功能单元库中各个功能模块的具体物理信息,在多供电电压、多调度周期模式下,建立了整数线性规划模型及其相应的6组约束条件,并将高层次综合中的调度过程和功能单元的绑定过程统一起来进行了模型化.文中算法已经成功地应用到自行开发的高层次综合工具之中,算法中得到的数据结果可以直接用于下一步的布图规划.对测试用例的实验进一步说明:同时优化峰值周期功耗和峰值模块功耗可以取得更好的综合结果,并且改善功耗时延乘积项的值(平均降低了30%),提高电路的可靠性和稳定性.     

高层次数据流的多电压低功耗综合方法

为了有效地进行算法行为描述到寄存器传输级结构描述的转换,提出一种同时考虑功能单元功耗、互连功耗和电压转换功耗的模型和基于网络流的低功耗设计方法.首先对给定的数据流图进行单电压高层次综合,然后对单电压综合结果迭代地进行多电压调整;提取每次迭代时需要调整的网络流子图,对该子图运行最小费用最大流增量算法.该方法充分利用前面迭代中得到的优化解,避免了对整个网络流的重复计算.实验结果表明,文中方法在互连功耗、电压转换功耗和总功耗等方面均有较大优化.     

基于多核处理器的K线程低能耗的任务调度优化算法

针对具有独立DVFS的多核处理器系统,提出了一种K线程低能耗模型的并行任务调度优化算法(Tasks Optimization based on Energy-Effectiveness Model,TO-EEM)。与传统的并行任务节能调度相比,该算法的主要目标是不仅通过降低处理器频率来减少处理器瞬时功耗,而且结合并行任务间的同步互斥所造成的线程阻塞情况,合理分配线程资源来减少线程同步时间,优化并行性能;保证任务在一定的并行加速比性能前提下,提高资源利用率,减少能耗,达到程序能耗和性能之间的折衷。文中进行了大量模拟实验,结果证明提出的任务优化模型算法节能效果明显,能有效降低处理器的功耗,并始终保持线性加速比。     

可靠性云计算环境下能源效率的提高机制

针对当前云计算能源效率低以及电源故障等不可靠问题,提出了一种物理主机整合机制以及调度算法,在保障云计算可靠性的同时提高能源效率。能量优化机制可以察觉优化时机,在电源等故障时执行调度算法。算法调节虚拟机到物理主机的映射,同时将相应物理主机中空闲的CPU容量,分配到正在运行的虚拟机中,从而提高能源效率。实验结果表明,与传统的调度算法相比,该算法在工作效率上提高了15.8%,在能量消耗上降低了9.8%。     

基于遗传算法的IP核测试调度优化

测试调度能够很好的减少测试时间和降低测试成本.通过调度,SOC中尽可能多的IP核可以进行并行测试,然而过度的并行测试会引起功耗过高,对SOC产生不利影响.为了改善这个问题,考虑峰值功耗因素的限制,提出一种基于遗传算法的IP核测试调度优化方案,寻求最短测试时间.通过对ISCAS标准电路组成的SOC进行仿真实验,验证了该方...     

Overlay networks for task allocation and coordination in large-scale networks of cooperative agents

This paper proposes a novel method for scheduling and allocating atomic and complex tasks in large-scale networks of homogeneous or heterogeneous cooperative agents. Our method encapsulates the concepts of searching, task allocation and scheduling seamlessly in a decentralized process where no accumulated or centralized knowledge or coordination is necessary. Efficient searching for agent groups that can facilitate the scheduling of tasks is accomplished through the use of a dynamic overlay structure of gateway agents and the exploitation of routing indices. The task allocation and the scheduling of complex tasks are accomplished by combining dynamic reorganization of agent groups and distributed constraint optimization methods. Experimental results display the efficiency of the proposed method.     

An energy-efficient reliable grid scheduling model using NSGA-II

Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Non-dominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multi-objective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.     

Algorithms for high-level synthesis

Synthesis algorithms that offer a technique for scheduling operations and allocating registers and buses in light of both timing constraints and available hardware resources are presented. They enhance current scheduling techniques by using a global priority function that minimizes storage, interconnections, and functional unit cost. Algorithms for allocating registers and buses minimize storage and interconnection costs and take into account the interdependence of both tasks. The algorithms are also applicable to more than one method of synthesis; although first implemented in the HAL system, they have since been integrated into more specialized high-level synthesis systems     

Complex componential approach for redundancy allocation problem solved by simulation-optimization framework

This article addresses the problem of redundancy and reliability allocation in the operational dimensioning of an automated production system. The aim of this research is to improve the global reliability of the system by allocating alternative components (redundancies) that are associated in parallel with each original component. By considering a complex componential approach that simultaneously evaluates the interrelations among sub-systems, conflicting goals, and variables of different natures, a solution for the problem is proposed through a multi-objective formulation that joins a multi-objective elitist genetic algorithm with a high-level simulation environment also known as simulation optimization framework.     

Feedback scheduling of priority-driven control networks

With traditional open-loop scheduling of network resources, the quality-of-control (QoC) of networked control systems (NCSs) may degrade significantly in the presence of limited bandwidth and variable workload. The goal of this work is to maximize the overall QoC of NCSs through dynamically allocating available network bandwidth. Based on codesign of control and scheduling, an integrated feedback scheduler is developed to enable flexible QoC management in dynamic environments. It encompasses a cascaded feedback scheduling module for sampling period adjustment and a direct feedback scheduling module for priority modification. The inherent characteristics of priority-driven control networks make it feasible to implement the proposed feedback scheduler in real-world systems. Extensive simulations show that the proposed approach leads to significant QoC improvement over the traditional open-loop scheduling scheme under both underloaded and overloaded network conditions.     

基于混合粒子群优化的CMP线程调度方法

为提高片上多核处理器(CMP)架构中线程调度的执行效率,发挥CMP的并行性能,提出一种基于混合粒子群优化算法的线程调度方法.根据设计的线程调度模型,利用有向无环图表述线程及线程间的相互依赖关系,并采用改进的混合粒子群算法对其进行合理调度.实验结果表明,该方法的执行效率优于现有的遗传算法,能有效地降低任务的执行时间,充分发挥多核架构的优势.