An application of fuzzy logic for hardware/software partitioning in embedded systems

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases.

Algoritmos metaheurísticos en el problema del particionado hardware/software de sistemas embebidos

El particionado hardware/software es una tarea fundamental en el co-diseño de sistemas embebidos. En ella se decide, teniendo en cuenta las métricas de diseño, qué componentes se ejecutarán en un procesador de propósito general (software) y cuáles en un hardware específico. En los últimos años se han propuesto diversas soluciones al problema del particionado dirigidas por algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, la elección del algoritmo más apropiado sigue siendo un problema abierto. En este trabajo se presenta una comparación de seis algoritmos metaheurísticos: Búsqueda aleatoria (Random search), Búsqueda tabú (Tabu search), Recocido simulado (Simulated annealing), Escalador de colinas estocástico (Stochastic hill climbing), Algoritmo genético (Genetic algorithm) y Estrategia evolutiva (Evolution strategy). El modelo utilizado en la comparación está dirigido a minimizar el área ocupada y el tiempo de ejecución, las restricciones del modelo son consideradas como penalizaciones para incluir en el espacio de búsqueda otras soluciones. Los resultados muestran que los algoritmos Escalador de colinas estocástico y Estrategia evolutiva son los que mejores resultados obtienen en general, seguidos por el Algoritmo genético.

Biogeographical Patterns and Phenological Changes in Lapiedra martinezii Lag. Related to Its Alkaloid Diversity

The aim of this work was to investigate the alkaloid patterns of Lapiedra martinezii and their relation to biogeography and phenology focused in a phylogenetic comparison. Plants from 14 populations of L. martinezii, covering almost its entire distribution area, were subjected to morphological, ecological, and phytochemical analysis. Experiments for different alkaloid-type content are proposed as a new tool for analysis of plant distribution. Several plants were transplanted for weekly observation of their phenological changes, and alkaloids from different plant organs were extracted, listed, and compared. The alkaloid pattern of L. martinezii comprises 49 compounds of homolycorine, lycorine, tazettine, haemantamine, and narciclasine types. The populations located in the north and south margins of the distribution area displayed alkaloid patterns different from those of the central area. Changes in these patterns during their phenological cycle may be related to a better defence for plant reproduction. L. martinezii is an old relict plant, and it has maintained some of the more primitive morphological features and alkaloid profiles of the Mediterranean Amaryllidaceae. The variations in alkaloid content observed could be interpreted in a phylogenetic sense, and those found in their phenological changes, in an adaptive one.