Hardware/Software Co-design for Particle Swarm Optimization Algorithm

[[abstract]]This paper presents a hardware/software (HW/SW) co-design approach using SOPC technique and pipeline design method to improve the performance of particle swarm optimization (PSO) for embedded applications. Based on modular design architecture, a particle updating accelerator module via hardware implementation for updating velocity and position of particles and a fitness evaluation module implemented on a soft-cored processor for evaluating the objective functions are respectively designed and work closely together to accelerate the evolution process. Thanks to a flexible design, the proposed approach can tackle various optimization problems of embedded applications without the need for hardware redesign. To compensate the deficiency in generating truly random numbers by hardware implementation, a particle re-initialization scheme is also presented in this paper to further improve the execution performance of the PSO. Experiment results have demonstrated that the proposed HW/SW co-design approach to realize PSO is capable of achieving a high-quality solution effectively.[[conferencetype]]國際[[conferencedate]]20101010~20101013[[iscallforpapers]]Y[[conferencelocation]]Istanbul, Turke

Hardware/Software Co-design of Particle Filter and Its Application in Object Tracking

[[abstract]]This paper presents a hardware/software co-design method for particle filter based on System On Program Chip (SOPC) technique. Considering both the execution speed and design flexibility, we use a NIOS II processor to calculate weight for each particle and a hardware accelerator to update particles. As a result, execution efficiency of the proposed hardware/software co-design method of particle filter is significantly improved while maintaining design flexibility for various applications. To demonstrate the performance of the proposed approach, a real-time object tracking system is established and presented in this paper. Experimental results have demonstrated the proposed method have satisfactory results in real-time tracking of objects in video sequences.[[conferencetype]]國際[[conferencedate]]20110608~20110610[[conferencelocation]]Macao, Chin

Parallel Implementation of Particle Swarm Optimization on FPGA

This brief proposes a parallel implementation, with fixed point, of the particle swarm optimization (PSO) algorithm on field-programmable gate array (FPGA). Results associated with the processing time and area occupancy on FPGA for several numbers of particles and dimensions were analyzed. Studies concerning the accuracy of the PSO response for the optimization problem using the Rastrigin function were also analyzed for the hardware implementation. The project was developed on the Virtex-6 xc6vcx240t 1ff1156 FPGA

Hardware/software partitioning algorithm based on the combination of genetic algorithm and tabu search

To solve the hardware/software (HW/SW) partitioning problem of a single Central Processing Unit (CPU) system, a hybrid algorithm of Genetic Algorithm (GA) and Tabu Search(TS) is studied. Firstly, the concept hardware orientation is proposed and then used in creating the initial colony of GA and the mutation, which reduces the randomicity of initial colony and the blindness of search. Secondly, GA is run, the crossover and mutation probability become smaller in the process of GA, thus they not only ensure a big search space in the early stages, but also save the good solution for later browsing. Finally, the result of GA is used as initial solution of TS, and tabu length adaptive method is put forward in the process of TS, which can improve the convergence speed. From experimental statistics, the efficiency of proposed algorithm outperforms comparison algorithm by up to 25% in a large-scale problem, what is more, it can obtain a better solution. In conclusion, under specific conditions, the proposed algorithm has higher efficiency and can get better solutions

Heterogeneous architecture to process swarm optimization algorithms

Desde años recientes, el paralelismo hace parte de la arquitectura de las computadoras personales al incluir unidades de co-procesamiento como las unidades de procesamiento gráfico, para conformar así una arquitectura heterogénea. Este artículo presenta la implementación de algoritmos de enjambres sobre esta arquitectura para resolver problemas de optimización de funciones, destacando su estructura inherentemente paralela y sus propiedades de control distribuido. En estos algoritmos se paralelizan los individuos de la población y las dimensiones del problema gracias a la granuralidad del sistema de procesamiento, que además proporciona una baja latencia de comunicaciones entre los individuos debido al procesamiento embebido. Para evaluar las potencialidades de los algoritmos de enjambres sobre la plataforma heterogénea, son implementados dos de ellos: el algoritmo de enjambre de partículas y el algoritmo de enjambre de bacterias. Se utiliza la aceleración como métrica para contrastar los algoritmos en la arquitectura heterogénea compuesta por una GPU NVIDIA GTX480 y una unidad de procesamiento secuencial, donde el algoritmo de enjambre de partículas obtiene una aceleración de hasta 36,82x y el algoritmo de enjambre de bacterias logra una aceleración de hasta 9,26x. Además, se evalúa el efecto al incrementar el tamaño en las poblaciones donde la aceleración es significativamente diferenciable pero con riesgos en la calidad de las soluciones.Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution

進化型ハードウェアのアーキテクチャに関する研究

東京電機大学201

Hardware/software co-design for particle swarm optimization algorithm

[[abstract]]This paper presents a hardware/software (HW/SW) co-design approach using SOPC technique and pipeline design method to improve design flexibility and execution performance of particle swarm optimization (PSO) for embedded applications. Based on modular design architecture, a Particle Updating Accelerator module via hardware implementation for updating velocity and position of particles and a Fitness Evaluation module implemented either on a soft-cored processor or Field Programmable Gate Array (FPGA) for evaluating the objective functions are respectively designed to work closely together to carry out the evolution process at different design stages. Thanks to the design flexibility, the proposed approach can tackle various optimization problems of embedded applications without the need for hardware redesign. To further improve the execution performance of the PSO, a hardware random number generator (RNG) is also designed in this paper in addition to a particle re-initialization scheme to promote exploration search during the optimization process. Experimental results have demonstrated that the proposed HW/SW co-design approach for PSO algorithms has good efficiency for obtaining high-quality solutions for embedded applications.[[incitationindex]]SCI[[booktype]]紙

Hardware/Software Co-design for Particle Swarm Optimization Algorithm

[[abstract]]This paper presents a hardware/software (HW/SW) co-design approach using SOPC technique and pipeline design method to improve the performance of particle swarm optimization (PSO) for embedded applications. Based on modular design architecture, a particle updating accelerator module via hardware implementation for updating velocity and position of particles and a fitness evaluation module implemented on a soft-cored processor for evaluating the objective functions are respectively designed and work closely together to accelerate the evolution process. Thanks to a flexible design, the proposed approach can tackle various optimization problems of embedded applications without the need for hardware redesign. To compensate the deficiency in generating truly random numbers by hardware implementation, a particle re-initialization scheme is also presented in this paper to further improve the execution performance of the PSO. Experiment results have demonstrated that the proposed HW/SW co-design approach to realize PSO is capable of achieving a high-quality solution effectively.