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。     

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.     

基于混沌PSO的动态可重构系统软硬件划分

软硬件划分是动态可重构系统软硬件协同设计中的关键技术之一,如何兼顾划分效率和划分效果,达到两者的最佳结合是软硬件划分的主要问题.在考虑动态部分重构及重构延时等特征的基础上,提出一种微粒群优化算法与混沌优化算法相结合的混沌微粒群软硬件划分方法.该算法使用基于实数编码的微粒群优化算法执行全局搜索,再根据搜索结果采用混沌优化算法执行局部搜索,具有较强的全局搜索和跳出局部最优的能力.仿真实验表明,该算法比标准微粒群算法和遗传算法具有更好的有效性和快速性,能够有效地实现应用任务图到可重构系统的时空映射.     

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。     

基于EDA的嵌入式系统软硬件划分方法

针对嵌入式系统软硬件协同设计中的软硬件划分问题,提出了一种基于分布估计算法的解决方案,通过将算法映射到一般结构Gauss网络上,提高了算法的稳定性和搜索效率.结果表明,该算法有效地解决了软硬件划分问题.     

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.     

基于权重可变免疫算法的动态可重构任务划分

基于FFGA的动态可重构系统能够在系统运行期间通过动态调整硬件资源来适应应用问题,从而满足嵌入式系统对性能、灵活性和成本越来越严格的要求.系统可动态加载配置文件的特点给系统软硬件任务的划分带来了新的问题.在充分考虑动态可重构系统特点的基础上,通过动态改变目标函数权重系数来适应可重构的变化,并运用于免疫算法对系统软硬件任务进行划分.实验结果表明,提出的划分方法除了能更贴近实际的系统外还具有较高的性能.     

搜索空间平滑技术在软硬件划分中的应用

软硬件划分问题是软硬件协同设计的重要问题之一,它涉及到系统建模,划分算法和划分方案评价等问题,其中划分算法设计是关键点。以提高系统时间性能为目标,利用任务流图构造系统模型,在其上实现了基于优先权的评价函数,提出了搜索空间平滑技术与离散粒子群算法相结合的软硬件划分算法,并且解决了两者的融合问题,并能根据系统信息动态适应调整算法参数。实验结果表明,算法时间开销稳定,求解质量较高。     

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%。     

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。     

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 %.     

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.     

一种基于SoC平台设计的AVS解码器软硬件分区结构

视频解码芯片的结构因硬件强大的处理能力和软件灵活的可编程功能从硬件转向软硬件分区结构。该文针对AVS标准的算法和解码实现复杂程度,根据软硬件协同设计思想提出了一种结构划分合理的AVS高清视频解码器软硬件分区结构。根据AVS算法的特点该结构将宏块层以上部分的元素解析划归到软件解码中,将宏块层解码划为硬件处理。经验证,该结构设计可实现AVS高清码流解码,并在C语言编写的硬件平台仿真程序中得以实现。     

嵌入式系统芯片中SM2算法软硬件协同设计与实现

针对现有的椭圆曲线算法系统级设计中开发周期长,以及不同模块的性能开销指标不明确等问题,提出一种基于电子系统级(ESL)设计的软硬件(HW/SW)协同设计方法.该方法通过分析SM2(ShangMi2)算法原理与实现方式,研究了不同的软硬件划分方案,并采用统一建模语言SystemC对硬件模块进行周期精确级建模.通过模块级与系统级两层验证比较软硬件模块执行周期数,得出最佳性能划分方式.最后结合算法控制流程图(CFG)与数据流程图(DFG)将ESL模型转化为寄存器传输级(RTL)模型进行逻辑综合与比较,得出在180 nm CMOS工艺,50 MHz频率下,当算法性能最佳时,点乘模块执行时间为20 ms,门数83 000,功耗约2.23 mW.实验结果表明所提系统级架构分析对基于椭圆曲线类加密芯片在性能、面积与功耗的评估优势明显且适用性强,基于此算法的嵌入式系统芯片(SoC)可根据性能与资源限制选择合适的结构并加以应用.     

New Model and Algorithm for Hardware/Software Partitioning

This paper focuses on the algorithmic aspects for the hardware/software （HW/SW） partitioning which searches a reasonable composition of hardware and software components which not only satisfies the constraint of hardware area but also optimizes the execution time. The computational model is extended so that all possible types of communications can be taken into account for the HW/SW partitioning. Also, a new dynamic programming algorithm is proposed on the basis of the computational model, in which source data, rather than speedup in previous work, of basic scheduling blocks are directly utilized to calculate the optimal solution. The proposed algorithm runs in O（n·A） for n code fragments and the available hardware area A. Simulation results show that the proposed algorithm solves the HW/SW partitioning without increase in running time, compared with the algorithm cited in the literature.     

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.     

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.