Power consumption is gradually becoming an important issue of designing computing systems. Most of the researches of low power issues have focused on semiconductor techniques or hardware architecture designs, but less utilized the techniques of software optimization. This paper presents a new scheduling methodology in source code level for Intelligent Memory System, which reduces the energy consumption by means of code compilation techniques. The scheduling kernel provides two options for users: performance-oriented low power scheduling and energy-oriented low power scheduling, to achieve the objective of considering high performance and low power issues. The experimental results are also presented and discussed.

L’objectif de cette thèse est d’étudier l’efficacité énergétique des stratégies basse consommation pour des plateformes représentatives. Principalement, nous nous intéresserons aux stratégies énergétiques pour des systèmes embarqués multicœur en étudiant le comportement de politiques logicielles qui permettent la réduction effective de l’énergie tout en répondant aux exigences applicatives. Le travail présenté dans ce mémoire vise à étudier des stratégies de gestion de la consommation pour des plateformes monoprocesseur puis multiprocesseur concrètes. L’approche utilisée pour cette étude fut basée sur des plateformes représentatives afin d’identifier les paramètres significatifs, aussi bien au niveau matériel qu’au niveau applicatif, à l’inverse de nombreux travaux dans lesquels ces paramètres sont assez peu pris en compte voir ignorés. Ce travail analyse et compare diverses expérimentations menées sur des politiques énergétiques basées sur des techniques DVFS (Dynamic Voltage and Frequency Scaling) et DPS (Dynamic Power Switching) et définit les conditions sous lesquelles ces stratégies sont efficaces. Ces expérimentations ont permis d’établir des conclusions remarquables qui peuvent servir de pré-requis lors de la définition de stratégies efficaces de gestion de la consommation. Ces résultats montrent également que pour obtenir des stratégies efficientes il est nécessaire de tenir compte du domaine applicatif. Enfin, il faut noter que les modèles de haut de niveau de consommation ont été définis sur la base des mesures effectuées et afin d’estimer les gains énergétiques dès les premières étapes d’un flot de conception. / The purpose of this study is to investigate how power management strategies can be efficiently exploited in actual platforms. Primarily, the challenges in multicore based embedded systems lies in managing the energy expenditure, determining the scheduling behavior and establishing methods to monitor power and energy, so as to meet the demands of the battery life and load requirements. The work presented in this dissertation is a study of low power-aware strategies in the practical world for single and multiprocessor platforms. The approach used for this study is based on representative multiprocessor platforms (real or virtual) to identify the most influential parameters, at hardware as well as application level, unlike many existing works in which these parameters are often underestimated or sometimes even ignored. The work analyzes and compares in detail various experimentations with different power policies based on Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Switching (DPS) techniques, and investigates the conditions at which these policies are effective in terms of energy savings. The results of these investigations reveal many interesting and notable conclusions that can serve as prerequisites for the efficient use of power management strategies. This work also shows the potential of advanced domain specific power strategies compared to real world available strategies that are general purpose based in their majority. Finally, some high level consumption models are derived from the different energy measurement results to let the estimation of power management benefits at early stages of a system development.

The purpose of this study is to investigate how power management strategies can be efficiently exploited in actual platforms. Primarily, the challenges in multicore based embedded systems lies in managing the energy expenditure, determining the scheduling behavior and establishing methods to monitor power and energy, so as to meet the demands of the battery life and load requirements. The work presented in this dissertation is a study of low power-aware strategies in the practical world for single and multiprocessor platforms. The approach used for this study is based on representative multiprocessor platforms (real or virtual) to identify the most influential parameters, at hardware as well as application level, unlike many existing works in which these parameters are often underestimated or sometimes even ignored. The work analyzes and compares in detail various experimentations with different power policies based on Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Switching (DPS) techniques, and investigates the conditions at which these policies are effective in terms of energy savings. The results of these investigations reveal many interesting and notable conclusions that can serve as prerequisites for the efficient use of power management strategies. This work also shows the potential of advanced domain specific power strategies compared to real world available strategies that are general purpose based in their majority. Finally, some high level consumption models are derived from the different energy measurement results to let the estimation of power management benefits at early stages of a system development.