嵌入式系统的软硬件划分

嵌入式系统软硬件协同设计中的关键步骤之一是软硬件划分。现有的许多软硬件划分方法都试图捕获太多有关划分问题和目标结构的细节，可扩展性差。本文提出了一种简化的软硬件划分问题模型，这种简化模型能分别对不同的划分问题进行形式化定义。在此模型的基础上，本文给出了基于ILP的算法和遗传算法。实验结果表明，我们的遗传算法能有效地解决千万个节点规模的划分问题，并获得近似最优解。     

可重构系统中软硬任务划分方法研究

软硬件任务划分是可重构系统开发过程中的重要设计步骤,其划分结果直接影响到可重构系统的性能。目前的软硬件任务划分技术大多只考虑了对应用程序或算法的划分结果,忽略了FPGA在配置和通信时的开销,从而导致实际应用效果不理想。介绍了一种基于性能评估的软硬件任务划分方法,即通过对FPGA计算开销、配置开销、通信开销的预评估测试,结合改进的模拟退火算法得出可重构系统中的软硬任务划分结果。实验结果表明,该划分方法具有较好的划分效果和算法收敛速度。     

A hardware/software partitioning algorithm based on artificial immune principles

Hardware/software codesign is the main approach to designing the embedded systems. One of the primary steps of the hardware/software codesign is the hardware/software partitioning. A good partitioning scheme is a tradeoff of some constraints, such as power, size, performance, and so on. Inspired by both negative selection model and evolutionary mechanism of the biological immune system, an evolutionary negative selection algorithm for hardware/software partitioning, namely ENSA-HSP, is proposed in this paper. This ENSA-HSP algorithm is proved to be convergent, and its ability to escape from the local optimum is also analyzed. The experimental results demonstrate that ENSA-HSP is more efficient than traditional evolutionary algorithm.     

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 %.     

Issues in distributed timed model checking

In this work we present Zeus, a distributed timed model checker that evolves from the TCTL model checker Kronos [13] and that currently can handle backwards computation of reachability properties [2] over timed automata [3].Zeus was developed following a software architecture-centric approach. Its conceptual architecture was conceived to be sufficiently modular to house several features such as a priori graph partitioning, synchronous and asynchronous computation, communication piggybacking, delayed messaging, and dead-time utilization.Surprisingly enough, early experiments pinpointed the difficulties of getting speedups using asynchronous versions and showed interesting results on the synchronous counterpart, although being intuitively less attractive.     

基于免疫算法的嵌入式系统软硬件划分方法

软硬件划分是嵌入式系统协同设计的关键问题之一。提出了一种划分模型，并通过改进的免疫算法解决了在多约束条件下软硬件划分的优化问题。在该免疫算法中，引入了免疫算子，通过从以往经验中提取疫苗，在生成子代过程中注入疫苗，使划分算法得到了优化。实验表明该算法具有较快的收敛速度，并且在总体性能上优于传统遗传算法。     

基于Token选取的OpenStack单一平面网络建模方法

CPN形式化建模适合为包含大量并发、通信、同步共享行为的软硬件系统建立形式模型,并完成系统功能和性能等方面的行为分析。在传统的CPN建模中,token的选取采用穷举法,由此造成生成的token数量较多、CPN模型生成的状态空间相当庞大甚至状态空间爆炸等问题。针对上述问题,提出将符号执行与CPN建模相结合,并在CPN模型的执行过程中采用一种基于token选取的方法,进而得到CPN模型的状态可达图。通过对OpenStack云平台支持创建的单一平面网络进行CPN建模,针对传统方法和所提方法生成的状态空间,分析了其规模的变化,验证了所提方法的有效性。     

约束驱动与松弛时间消除相结合的硬/软件划分算法

硬/软件划分是硬/软件协同设计的关键问题之一．在分析了已经被提出的硬/软件划分算法中存在的问题之后，提出了一种基于约束驱动和松弛时间消除相结合的硬/软件划分算法．首先是获取结点面积—时间(A—T)曲线的方法，然后比较时间约束紧迫度与阈值的大小，决定结点是用硬件还是软件执行．硬/软件面积的约束紧迫度决定硬/软件执行面积，通过A—T曲线找出对应的执行时间．最后，消除结点之间存在的松弛时间进一步优化设计．     

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。     

基于Hopfield神经网络和禁忌搜索的软/硬件功耗划分

在目前全球倡导“低碳经济”的背景下,随着嵌入式系统大量而广泛的使用,嵌入式软件功耗已成为嵌入式系统设计的一个关键因素,而软/硬件划分是嵌入式软件功耗优化的一种重要方法。首先在性能约束条件下,建立以嵌入式软件功耗为目标的软/硬件双路划分模型;然后,提出了一种基于离散Hopfield神经网络(HNN)和禁忌搜索(TS)融合的求解算法,采用离散Hopfield算法作为主算法能较快地获得可行解,使用禁忌搜索算法“禁忌”当前解而转移到目标函数的其他极小点,从而可跳出局部最优解而快速趋于全局最优解;最后,仿真实验表明,与同类算法相比,该算法不但具有搜索速度上的优势,而且求得全局最优解的概率更高。