Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

Abstract

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.

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

“…This paper [1] showed the usage of improved brain storming optimization algorithm (BSO) on hardware/software partitioning. In this BSO the traditional clustering algorithm (K-means) is modified and also compared with other optimization algorithms using 8 benchmark functions.…”

GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems

“…In (Ouyang et al, 2016), the proposed algorithm aimed to minimize the overall execution time of the system while meeting the constraint on the hardware area in a heterogeneous multi-processing system, concerning the parallel execution of hardware and software, the finishing time of the system can be defined as the finishing time of the critical path in a tree based graph. Another example of minimizing the execution time of the critical path in a multiple processing embedded system in given in (Zhang et al, 2019), the proposed algorithm is based on Brainstorm Optimization Algorithm.…”

“…In this article, the proposed approach has the goal of optimizing the total hardware area and the overall execution time of a multi-processing embedded system, as in (Zhang et al, 2018;Ouyang et al, 2016;Zhang et al, 2019), the optimization of the execution time consists of minimizing the execution time of the critical path in a DAG graph representing the system.…”

Brainstorm optimization for multi-document summarization

“…The key ideas of the BSO are mutation and clustering which is encouraging in searching ability to find global optimum and preserving population diversity. The BSO algorithm is applied to solve many real-world applications including data classification [19,20], multi-objective optimization problem [21], hierarchical clustering analysis [22], multi-strategy BSO for global optimization functions [23], image classification [24], hardware / software partitioning [25], renewal energy system [26].…”