Using Blind Optimization Algorithm for Hardware/Software Partitioning

Zhang, Tao; Zhao, Xin; An, Xinqi; Quan, Haojun; Lei, Zhichun

doi:10.1109/access.2017.2669481

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Building on the graph that represents data dependencies, an optimisation problem of assigning stages to hardware types can be formulated, similar to the approach targeting embedded systems by Zhang et al [177]. In essence, constraints are formulated so that each stage is assigned to the host or a device, resulting in an overall simulation schedule.…”

Section: Optimisation Problemmentioning

confidence: 99%

A Survey on Agent-based Simulation Using Hardware Accelerators

et al. 2019

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Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorization of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.

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Section: Optimisation Problemmentioning

confidence: 99%

A Survey on Agent-based Simulation Using Hardware Accelerators

et al. 2019

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“…In order to further demonstrate the performance of the IBSO, we introduce the quality gain α and the running time gain β in this paper. The calculation of α and β is shown in (6) and 7respectively.…”

Section: Comparisons Between Ibso and Several Heuristic Algorithmsmentioning

confidence: 99%

“…The SI algorithms have the advantages of excellent global search ability and strong robustness. They are suitable for solving complex problems because their optimization process can be seen as a black box [6]. The common SI algorithms include particle swarm optimization (PSO) [7], artificial bee colony (ABC) [8], artificial fish school algorithm (ASFA) [9], ant colony algorithm (ACO) [10], shuffled frog leaping algorithm (SFLA) [11] and so on.…”

Section: Introductionmentioning

confidence: 99%

Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

2019

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Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.

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“…The local dimming algorithm in our work [12] was designed based on evolutionary computation (EC). As an important branch of computational intelligence (CI), the EC algorithm has a strong global search ability and good performance in solving complex optimization problems [13]. EC algorithms such as Genetic Algorithm (GA) [14], Artificial Bee Colony (ABC) [15], Particle Swarm Optimization (PSO) [16], and Ant Colony Optimization (ACO) [17] have been successfully applied to different optimization areas.…”

Section: Introductionmentioning

confidence: 99%

Using the Guided Fireworks Algorithm for Local Backlight Dimming

et al. 2019

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Local backlight dimming is a promising display technology, with good performance in improving the visual quality and reducing the power consumption of device displays. To set optimal backlight luminance, it is important to design high performance local dimming algorithms. In this paper, we focused on improving the quality of the displayed image, and take local backlight dimming as an optimization problem. In order to better evaluate the image quality, we used the structural similarity (SSIM) index as the image quality evaluation method, and built the model for the local dimming problem. To solve this optimization problem, we designed the local dimming algorithm based on the Fireworks Algorithm (FWA), which is a new evolutionary computation (EC) algorithm. To further improve the solution quality, we introduced a guiding strategy into the FWA and proposed an improved algorithm named the Guided Fireworks Algorithm (GFWA). Experimental results showed that the GFWA had a higher performance in local backlight dimming compared with the Look-Up Table (LUT) algorithm, the Improved Shuffled Frog Leaping Algorithm (ISFLA), and the FWA.

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Using Blind Optimization Algorithm for Hardware/Software Partitioning

Cited by 11 publications

References 19 publications

A Survey on Agent-based Simulation Using Hardware Accelerators

A Survey on Agent-based Simulation Using Hardware Accelerators

Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

Using the Guided Fireworks Algorithm for Local Backlight Dimming

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