双NIOS Ⅱ软核系统在潮流能发电中的应用

开发海洋潮流能对缓解能源危机具有重要意义,电力变换系统是潮流能发电系统的关键技术之一。提出了一种利用SOPC技术实现潮流能发电电力变换控制系统的设计方法,采用单片高性能FPGA芯片构建双NIOSⅡ软核系统,其中一个NIOSⅡ处理器负责系统任务调度和能量管理,一个NIOSⅡ处理器负责电力变换控制和异常诊断。针对电力变换系统的信息数据特点,提出了基于RAM、FIFO、GPIO的双NIOSⅡ处理器握手机制,并给出了系统的具体设计实现。该双NIOSⅡ处理器系统提高了系统的任务调度灵活性、控制算法处理速度和应急处理能力。     

NIOS Ⅱ处理器中定制指令的设计与实现

概要介绍NIOS Ⅱ处理器,详述NIOS Ⅱ处理器中定制指令的硬件实现和软件接口。并结合实例说明在进行SOPC设计时,可以把强实时软件算法或费时的软件计算作为定制指令,加入到NIOS Ⅱ处理器指令集中,提高系统性能。     

基于NIOS Ⅱ处理器的 MIL-STD-1553B 接口卡设计

基于NIOSⅡ处理器搭建了可编程片上系统,在该系统中通过控制HI-6110实现了MIL-STD-1553B总线协议,通过双口RAM实现了与PCI总线的通信。重点论述了NIOSⅡ处理器系统的硬件和软件设计,双口RAM的地址空间划分,PCI9054的驱动软件设计。测试表明,用本方法设计的接口卡能很好地实现MIL-STD-1553B总线协议。     

基于NIOS Ⅱ处理器的四象限变流器控制

本文介绍了一种新的基于NIOSⅡ处理器的四象限变流器控制方案。建立了一种基于NIOSⅡ处理器的设计平台,并在其上进行了四象限变流器的仿真和设计,试验结果表明:基于NIOSⅡ处理器的四象限变流器控制能够达到预期的目的。     

基于NIOS Ⅱ的平台直方图均衡算法

NIOS Ⅱ软核处理器是Altera公司推出的一款灵活高效的嵌入式处理器。该处理器的应用常见于控制和通信领域。本文描述了在NIOS Ⅱ系统上实现平台直方图均衡算法(Plateau Equalization,PE)的设计方案。通过NIOS Ⅱ的自定制指令和C语言中嵌入汇编语言,成功的将PE算法在FPGA上实现,并且能够保证系统的运行效率和实时处理能力。     

基于NIOSⅡ软核处理器的嵌入式测试系统软硬件设计研究

介绍了利用NIOSⅡ软核处理器设计嵌入式测试系统的两类系统架构,详细讲述了基于NIOSⅡ软核处理器的嵌入式测试系统软硬件设计方法;最后结合EP2C8Q-208C8型FPGA芯片,利用Verilog语言描述A/D芯片的工作时序逻辑,利用NIOSⅡ软核处理器设计串口处理单元,将A/D采集的数据通过串口发送到计算机显示。实践表明,利用NIOS II软核处理器设计嵌入式测试系统,具有开发周期短,系统集成度高,功能灵活多样等特点,与传统利用单片机设计嵌入式测试系统相比,具有时钟频率高、运行速度快、调试方便等特点,是一种值得推广的嵌入式测试系统设计方法。     

基于FPGA的嵌入式系统的研究与应用

嵌入式系统是嵌入到对象体系中的专用计算机系统，包括硬件和软件两大部分。设计中使用的硬件是Altera公司开发的NIOSⅡ嵌入式处理器软核及用户逻辑。文章主要介绍FPGA和SOPC的特点及其发展趋势以及如何使用Quartus Ⅱ42和NIOSⅡIDE等开发工具在FPGA器件上实现SOPC的设计。     

循环冗余校验在SOPC中的自定义指令实现

NIOS Ⅱ软核处理器是Altera公司一款灵活高效的嵌入式处理器,常应用于控制和通信领域.循环冗余校验(CRC)广泛应用于各种数据校验中.本文通过NIOS Ⅱ的自定义指令,成功地将循环冗余校验并行算法在FPGA上实现.结果表明,加快了专项任务的执行,提高了系统的效率.     

基于SOPC的条形码识别系统设计

本文提出了一种基于NIOS Ⅱ嵌入式软核处理器的条形码识别系统的实现方法.该系统在altera公司的cyclone Ⅱ系列芯片上实现,采用sopc builder里的组件生成了片上系统作为控制核心,同时可以通过GPRS发送信息.     

自动指纹识别系统的硬件逻辑实现

介绍基于NIOSⅡ嵌入式处理器的自动指纹识别系统的实现方法;具体说明自动指纹识别系统的基本原理、硬件结构设计、用户自定制指令和SOPC系统设计;详细说明如何在SOPC开发工具中完成系统的硬件开发和在NIOSⅡIDE集成开发环境中完成系统的软件开发。     

EPTS:一种实时动态电压调整的抢占阈值调度器

低功耗目前已成为嵌入式实时系统设计中非常重要的性能需求。动态电压调度DVS机制通过动态调整处理器电压进而有效降低系统功耗,正在逐渐得到广泛应用。抢占阈值调度策略实现双优先级系统,每个任务具有两个优先级,任务优先级被用于任务之间竞争处理器,而抢占阈值作为任务开始运行后实际使用的优先级,从而减少现场切换次数,降低系统功耗,同时也提高整个任务集合的可调度性。本文提出一种在线节能调度算法EPTS,拓展抢占阈值调度模型,在任务执行过程中动态调节处理器电压,力求在保证任务集合可调度性的前提下尽可能减少系统功耗,提高系统性能。而后在AMDAthlon4处理器和RT-Linux平台上实现了EPTS调度器,实验证明对于实际任务集合能够有效节能,提高了处理器的利用率,改善了RT-Linux的实时性能。     

基于SoPC/NIOS Ⅱ的信号发生器设计与实现

运用基于NIOS Ⅱ嵌入式处理器的SoPC技术,设计了一个任意信号发生器,不仅可以输出正弦波、方波、三角波和锯齿波等常见波形,且各波形的频率和幅度可调,可根据用户需要进行现场编程,具有控制灵活、输出频率稳定、准确、波形质量好和输出频率范围宽等优点.     

SOPC技术在直序扩频收发机模块中频的应用

介绍了基于NIOSII软核处理器的SOPC技术,分析了传统方法和基于SOPC技术的方法实现扩频收发机的优劣,详细说明了嵌有NIOSII的SOPC技术的方案设计。该设计增强了系统功能,改善了系统的灵活性,并提高了其适应不同应用需求的伸缩性。     

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。     

Energy efficient scheduling of parallel tasks on multiprocessor computers

In this paper, scheduling parallel tasks on multiprocessor computers with dynamically variable voltage and speed are addressed as combinatorial optimization problems. Two problems are defined, namely, minimizing schedule length with energy consumption constraint and minimizing energy consumption with schedule length constraint. The first problem has applications in general multiprocessor and multicore processor computing systems where energy consumption is an important concern and in mobile computers where energy conservation is a main concern. The second problem has applications in real-time multiprocessing systems and environments where timing constraint is a major requirement. Our scheduling problems are defined such that the energy-delay product is optimized by fixing one factor and minimizing the other. It is noticed that power-aware scheduling of parallel tasks has rarely been discussed before. Our investigation in this paper makes some initial attempt to energy-efficient scheduling of parallel tasks on multiprocessor computers with dynamic voltage and speed. Our scheduling problems contain three nontrivial subproblems, namely, system partitioning, task scheduling, and power supplying. Each subproblem should be solved efficiently, so that heuristic algorithms with overall good performance can be developed. The above decomposition of our optimization problems into three subproblems makes design and analysis of heuristic algorithms tractable. A unique feature of our work is to compare the performance of our algorithms with optimal solutions analytically and validate our results experimentally, not to compare the performance of heuristic algorithms among themselves only experimentally. The harmonic system partitioning and processor allocation scheme is used, which divides a multiprocessor computer into clusters of equal sizes and schedules tasks of similar sizes together to increase processor utilization. A three-level energy/time/power allocation scheme is adopted for a given schedule, such that the schedule length is minimized by consuming given amount of energy or the energy consumed is minimized without missing a given deadline. The performance of our heuristic algorithms is analyzed, and accurate performance bounds are derived. Simulation data which validate our analytical results are also presented. It is found that our analytical results provide very accurate estimation of the expected normalized schedule length and the expected normalized energy consumption and that our heuristic algorithms are able to produce solutions very close to optimum.     

Energy-aware task migration for multiprocessor real-time systems

A task migration method is proposed for energy savings in multiprocessor real-time systems. The method is based on the portioned scheduling technique which classifies each task as a fixed task or a migratable task. The basic task migration problem with specific parameters is formulated as a linear programming problem to minimize average power. Then, the method is extended to more general case with a complete migration algorithm. Moreover, a scheduling algorithm is proposed for migratable tasks. Simulation results on two processor models demonstrated significant energy savings over existing methods.     

Feasible processor allocation in a Hard-Real-Time environment

Consider the problem of scheduling a set of preemptible tasks in one or more processor systems. The task system consists of a set of independent tasks or a task set with precedence relations. Each task is characterized by execution time and deadline. This article presents scheduling algorithms that guarantee all time constraints. These algorithms are so easy to implement that they can be used in real-time operating systems. An overview is given for the different feasible scheduling algorithms of some task and processor systems.     

Energy-aware mixed partitioning scheduling in standby-sparing systems

Previous standby-sparing techniques assume that all tasks don't access to shared resources. In addition, primary tasks and backup tasks are allocated to the primary processor and spare processor respectively. Spare processor schedules tasks with maximum processor speed. Unlike previous techniques, we have studied the problem of minimizing energy consumption and preserving the original reliability for dynamic-priority real-time task set with shared resources in a standby-sparing system. We propose a novel energy-aware mixed partitioning scheduling algorithm (EAMPSA). Earliest deadline first/dynamic deadline modification (EDF/DDM) scheduling scheme is used to ensure that the shared resources can be accessed in a mutual exclusive manner. Uniformly speed is used to the primary processor and the spare processor. In addition, we use the mixed mapping partitioning of primary and backup tasks method to map tasks. A novel method of mapping task is proposed i.e. the tasks which need to access to shared resources are mapped into the primary processor and the tasks which have no resource requirements are mapped into the spare processor. Furthermore, DVS and DPM techniques are used for both primary and backup tasks to save energy. The experimental results show that the EAMPSA algorithm consumes average 55.43% less energy than that of the SSPT algorithm.     

Task cluster scheduling in a grid system

Effective load distribution and resource management is of great importance in designing complex distributed systems as grid. This pre-assumes the capability of partitioning the arriving jobs into independent tasks that can be executed simultaneously, assigning the tasks to processors and scheduling the task execution on each processor. A simulation model, consisting of two homogeneous clusters, is considered to evaluate the performance for various workloads. The Deferred policy is applied to collect global system information about processor queues. This paper proposes a special scheduling method referred to as task clustering method. We examine the efficiency of two task routing policies – one static and one adaptive – and six task scheduling policies, which rearrange processor queues regarding to a criterion. Our simulation results indicate that the adaptive task routing policy in conjunction with SGFS-ST scheduling algorithm, which uses more efficiently the task clustering method, leads to a significant performance improvement.