基于NSGA—Ⅱ的嵌入式系统软硬件划分方法

软硬件划分是软硬件协同设计中的一个关键问题。针对单处理器嵌入式系统,提出将NSGA—Ⅱ应用于软硬件划分中,该算法一次运行可以获得多个Pareto最优解,为各个目标函数之间权衡分析提供了有效的工具,提高了设计效率。结果表明,通过该划分方法,在满足系统性能要求下,可为复杂嵌入式系统提供多个设计目标的全局优化方案。     

嵌入式系统软硬件自动划分方法研究

软硬件划分一直是嵌入式系统软硬件协同设计中的难点,如果离开具体系统,单纯的软硬件划分,其性能很难评估。本文提出基于系统体系结构,应用遗传算法来进行多目标优化的软硬件自动划分方法。在具体设计中,使用数据流图对系统建模,采用邻接表进行个体编码,定义交叉、变异操作,同时引入小生境技术,保持解的多样性。该方法为嵌入式系统软硬件自动划分提供一种新思路。     

一种嵌入式系统软硬件划分算法

软硬件划分是嵌入式系统软硬件协同设计中的一个关键问题.传统划分算法具有局部最优,收敛速度慢的缺陷.为使组成系统性能达到最优化,提出一种新的嵌入式系统软硬件划分算法.先采用嵌入式系统转化成有向无环图,可将嵌入式系统软硬件划分问题转换成一个多条件约束问题,用蚂蚁放置于有向无环图顶点上,对系统软硬件的划分准确率作为蚁群算法优化目标,通过蚁群算法搜索最优目标函数值,有效避免传统划分算法搜索陷入局部最小,大幅度降低搜索时间.实验结果表明,采用蚁群算法能够高效、快速获得准确地划分结果,为嵌入式系统设计提供了依据.     

基于组合算法的嵌入式系统软硬件划分方法

嵌入式系统软硬件划分是一个多约束条件、多目标的组合优化问题,单一算法难以找到最优设计方案,为此,提出一种遗传算法和粒子群算法组合的嵌入式系统软硬件划分方法。首先建立嵌入式系统软硬件划分问题的数学模型,然后利用遗传算法找到问题的可行解,最后采用粒子群算法找到最优方案,并采用仿真实验测试算法的性能。仿真结果表明,该方法提高了嵌入式系统软硬件划分问题的求解效率,可以快速找到更优的软硬件划分方案。     

软硬件协同设计中的软硬件划分方法综述

近年来,随着信息领域的物联网、工业互联网、机器人等研究热点发展,嵌入式系统技术再次得到科技工作者和工程师的广泛关注和重视,同时嵌入式系统产品的集成度和性能要求越来越高.软硬件协同设计是开发嵌入式系统产品的重要方法之一,而软硬件划分是软硬件协同设计中的关键技术.本文对现有软硬件划分方法从不同层面进行梳理和分类,重点介绍几种常用的软硬件划分方法,并结合实例进行了详细阐述,最后对这几种方法进行综合比较,供嵌入式系统开发科技工作者和工程师参考.     

基于多代理系统的软硬件协同设计

为使软硬件协同设计过程更具分布性、自主性及并行性,在软硬件协同设计中引入多代理(MAS)技术,提出软硬件协同设计的MAS模型,包括系统描述Agent、软硬件划分及映射Agent、软硬件设计Agent、协同通信Agent、性能评估Agent和硬件系统测试Agent的构建和应用。采用多个目标代理映射、协商的方法协调整个协同设计过程。实际应用表明,该方法能优化系统级芯片设计方案、软硬件结构和功能,并提高系统整体性能。     

软硬件协同设计方法的研究

论述了嵌入式系统软硬件协同设计的一般方法，结合CORSAIR、COOL和POLIS 3种有代表性的软硬件协同设计系统，对系统描述、软硬件划分、软硬件协同综合等几个主要设计步骤进行了研究与分析，并提出了新的思路和方法。     

三种软硬件划分算法的比较分析

软硬件划分是嵌入式系统软硬件协同设计中的关键技术之一,如何兼顾系统的性能和成本,达到两者的最佳结合,是软硬件划分的主要问题.针对单CPU多ASICs类型的目标结构,选取了遗传算法、禁忌搜索算法和模拟退火算法等全局优化算法进行系统的软硬件划分,并对3种算法的有效性进行了比较分析.     

基于量子粒子群算法的可重构系统软硬件划分

针对软硬件协同设计中软硬件划分的这个关键问题,提出了一种基于量子粒子群算法的动态可重构系统软硬件划分的算法;首先使用有向无环图对嵌入式系统建模,得到软硬件划分优化系统的目标函数;然后通过采用自适应的量子旋转角调整策略以及引入量子变异操作,有效避免搜索过程陷入局部最优,提高搜索效率;对比实验结果表明本文算法对解决软硬件划分问题的有效性;文章算法不但能够以更快的搜索速度得到软硬件划分结果,并且得到划分结果更优,是一种具有较高性能的划分方法.     

改进量子多目标进化算法用于SOC软硬件划分*

采用量子多目标进化算法对从任务级进行抽象建模所得到的系统模型进行软硬件划分,并针对SOC系统设计中存在的特点,对量子多目标进化算法进行改进。采用量子个体编码方案,避免个体编/解码的冗余。并将Pareto最优概念与多目标优化相结合,从而实现了兼顾系统面积、功耗、时间等参数的软硬件划分方法。仿真对比实验结果表明,该算法一次运行可以获得多个Pareto最优解,为各个目标函数之间权衡分析提供了有效的工具,提高了设计效率。在满足系统性能要求下,可为复杂SOC系统提供多个设计目标的全局优化方案。     

A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization

With the development of the design complexity in embedded systems, hardware/software (HW/SW) partitioning becomes a challenging optimization problem in HW/SW co-design. A novel HW/SW partitioning method based on position disturbed particle swarm optimization with invasive weed optimization (PDPSO-IWO) is presented in this paper. It is found by biologists that the ground squirrels produce alarm calls which warn their peers to move away when there is potential predatory threat. Here, we present PDPSO algorithm, in each iteration of which the squirrel behavior of escaping from the global worst particle can be simulated to increase population diversity and avoid local optimum. We also present new initialization and reproduction strategies to improve IWO algorithm for searching a better position, with which the global best position can be updated. Then the search accuracy and the solution quality can be enhanced. PDPSO and improved IWO are synthesized into one single PDPSO-IWO algorithm, which can keep both searching diversification and searching intensification. Furthermore, a hybrid NodeRank (HNodeRank) algorithm is proposed to initialize the population of PDPSO-IWO, and the solution quality can be enhanced further. Since the HW/SW communication cost computing is the most time-consuming process for HW/SW partitioning algorithm, we adopt the GPU parallel technique to accelerate the computing. In this way, the runtime of PDPSO-IWO for large-scale HW/SW partitioning problem can be reduced efficiently. Finally, multiple experiments on benchmarks from state-of-the-art publications and large-scale HW/SW partitioning demonstrate that the proposed algorithm can achieve higher performance than other algorithms.     

Algorithmic aspects for multiple-choice hardware/software partitioning

Hardware–software partitioning (HW/SW) divides an application into software and hardware. It is one of the crucial steps in embedded system design. For a given task, hardware with different areas may provide different execution speeds due to the potential of parallel execution in hardware implementation. Thus, one task may have multiple-choice in hardware implementation according to the available hardware areas. Existing HW/SW partitioning approaches typically consider only a single implementation manner in hardware, overlooking the multiple-choice of hardware implementations. This paper presents a computing model to cater for the HW/SW partitioning problems with the multiple-choice implementation in hardware. An efficient heuristic algorithm is proposed to rapidly generate approximate solution, that is further refined by a tabu search algorithm also customized in this paper. Moreover, a dynamic programming algorithm is proposed for the exact solution of the relatively small problems. Extensive simulation results show that the approximate solutions are very close to the exact ones, and they can be refined by tabu search to the solutions with the error no more than 1.5% for all cases considered in this paper.     

多选择软硬件划分问题的计算模型与动态规划算法

软硬件划分是软硬件协同设计的关键环节,划分的结果直接影响目标系统的设计质量。因此,对于一个给定的应用程序,为了使得目标系统快速执行且成本低廉,合理的划分策略十分重要。由于单个任务具有多种不同的硬件实现方式,与传统的单一硬件实现方式的软硬件划分问题相比,多选择的软硬件划分更能客观地反映现实应用。这导致问题的求解更具挑战性,它们已被证明是NP完全问题。基于多核处理器片上系统并针对任务图为二叉树的应用,建立了多选择软硬件划分问题的计算模型,并提出了解决该问题的动态规划算法。实验结果表明,当问题规模适中时,所提动态规划算法能够有效地获得精确解,并展示了算法的计算能力与硬件面积限制之间的关系。     

一种基于DQCGA算法的软硬件动态划分方法

软硬件划分是嵌入式系统设计的高层抽象环节中最重要的关键步骤之一.在某些数据相关的应用领域中,划分环境是动态变化的,因此我们提出了一种解决动态软硬件划分的方法.这种方法基于一种名为DQCGA的演化算法.DQCGA算法受自然界中对称和互补机制的启发,操纵一对互补的概率向量来适应动态变化的环境.我们系统地完成了建模,动态环境定义等环节,然后通过和已有方法的比较,有针对性地设计了实验.试验结果很好地证明了该方法对于解决软硬件划分问题的可行性和有效性,并且较之以往的方法有着更好的表现.     

改进PSO算法在软/硬件划分中的应用

针对嵌入式系统中的单MPU和单ASIC体系结构问题,提出一种改进粒子群算法,将该算法应用到数字音视频解码器的软/硬件划分中,一次运行可以获得较多Pareto最优解。讨论目标函数、系统约束、粒子比较准则、拥挤距离函数、变异算子和粒子适应度等问题的处理。实验结果表明,该算法改善了传统算法产生未成熟收敛、较少Pareto最优解和Pareto最优解前端分布不均匀的问题,增强算法的自适应性及结果的全局最优性。     

基于贪心算法和模拟退火算法的软硬件划分

软硬件划分是嵌入式系统设计过程中一个关键环节,已经被证明是一个NP问题。针对目前算法在进行大任务集下的软硬件划分时计算复杂度高、不能快速收敛,且找到的全局最优解的质量不佳等问题,提出一种基于贪心算法和模拟退火算法相融合的软硬件划分方法。首先将软硬件划分问题规约为变异的0-1背包问题,在求解背包问题的算法基础上用贪心算法构造出初始划分解;然后,对代价函数的解空间进行合理的区域划分,并基于划分的区间设计新的代价函数,采用改进的模拟退火算法对初始划分进行全局寻优。实验结果表明,与目前已有的类似改进算法相比,新算法在任务划分质量和算法运行时间两个方面的提升率最大可达到8%和17%左右,具有高效性和实用性。     

A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking

Sequential Monte Carlo (SMC) represents a principal statistical method for tracking objects in video sequences by on-line estimation of the state of a non-linear dynamic system. The performance of individual stages of the SMC algorithm is usually data-dependent, making the prediction of the performance of a real-time capable system difficult and often leading to grossly overestimated and inefficient system designs. Also, the considerable computational complexity is a major obstacle when implementing SMC methods on purely CPU-based resource constrained embedded systems. In contrast, heterogeneous multi-cores present a more suitable implementation platform. We use hybrid CPU/FPGA systems, as they can efficiently execute both the control-centric sequential as well as the data-parallel parts of an SMC application. However, even with hybrid CPU/FPGA platforms, determining the optimal HW/SW partitioning is challenging in general, and even impossible with a design time approach. Thus, we need self-adaptive architectures and system software layers that are able to react autonomously to varying workloads and changing input data while preserving real-time constraints and area efficiency. In this article, we present a video tracking application modeled on top of a framework for implementing SMC methods on CPU/FPGA-based systems such as modern platform FPGAs. Based on a multithreaded programming model, our framework allows for an easy design space exploration with respect to the HW/SW partitioning. Additionally, the application can adaptively switch between several partitionings during run-time to react to changing input data and performance requirements. Our system utilizes two variants of a add/remove self-adaptation technique for task partitioning inside this framework that achieve soft real-time behavior while trying to minimize the number of active cores. To evaluate its performance and area requirements, we demonstrate the application and the framework on a real-life video tracking case study and show that partial reconfiguration can be effectively and transparently used for realizing adaptive real-time HW/SW systems.     

Efficient heuristic and tabu search for hardware/software partitioning

Hardware/software (HW/SW) partitioning is a crucial step in HW/SW codesign that determines which components of the system are implemented on hardware and which ones on software. It has been proved that the HW/SW partitioning problem is NP-hard. In this paper, we present two approaches for HW/SW partitioning that aims to minimize the hardware cost while taking into account software and communication constraints. The first is a heuristic approach that treats the HW/SW partitioning problem as an extended 0–1 knapsack problem. In the second approach, tabu search is used to further improve the solution obtained from the proposed heuristic algorithm. Experimental results show that the proposed algorithms outperform a recently reported work by up to 28 %.     

高效软硬件划分算法及其提升技术

软硬件划分是软硬件协同设计的关键环节,它决定系统中哪些组件由软件实现,哪些由硬件实现。软硬件划分问题已被证明是NP完全问题。将一类软硬件划分问题看作变异的0-1背包问题,在求解背包问题的算法基础上构造出软硬件划分问题的优质启发解。此外,采用禁忌搜索(Tabu Search)算法对求得的启发解进行改进,在软件开销和通信开销满足一定约束的条件下,使得硬件开销尽可能小。实验结果证明,所提算法对当前最新算法的改进最大可达到28%。