The work presented in this thesis aims at validating an original multicriteria performances estimation tool, NESSIE, dedicated to the prediction of performances to accelerate the design of electronic embedded systems. This tool has been developed in a previous thesis to cope with the limitations of existing design tools and offers a new solution to face the growing complexity of the current applications and electronic platforms and the multiple constraints they are subjected to. More precisely, the goal of the tool is to propose a flexible framework targeting embedded systems in a generic way and enable a fast exploration of the design space based on the estimation of user-defined criteria and a joint hierarchical representation of the application and the platform. In this context, the purpose of the thesis is to put the original framework NESSIE to the test to analyze if it is indeed useful and able to solve current design problems. Hence, the dissertation presents : - A study of the State-of-the-Art related to the existing design tools. I propose a classification of these tools and compare them based on typical criteria. This substantial survey completes the State-of-the-Art done in the previous work. This study shows that the NESSIE framework offers solutions to the limitations of these tools. - The framework of our original mapping tool and its calculation engine. Through this presentation, I highlight the main ingredients of the tool and explain the implemented methodology. - Two external case studies that have been chosen to validate NESSIE and that are the core of the thesis. These case studies propose two different design problems (a reconfigurable processor, ADRES, applied to a matrix multiplication kernel and a 3D stacking MPSoC problem applied to a video decoder) and show the ability of our tool to target different applications and platforms. The validation is performed based on the comparison of a multi-criteria estimation of the performances for a significant amount of solutions, between NESSIE and the external design flow. In particular, I discuss the prediction capability of NESSIE and the accuracy of the estimation. -The study is completed, for each case study, by a quantification of the modeling time and the design time in both flows, in order to analyze the gain achieved by our tool used upstream from the classical tool chain compared to the existing design flow alone. The results showed that NESSIE is able to predict with a high degree of accuracy the solutions that are the best candidates for the design in the lower design flows. Moreover, in both case studies, modeled respectively at a low and higher abstraction level, I obtained a significant gain in the design time. However, I also identified limitations that impact the modeling time and could prevent an efficient use of the tool for more complex problems. To cope with these issues, I end up by proposing several improvements of the framework and give perspectives to further develop the tool.

The work presented in this thesis aims at validating an original multicriteria performances estimation tool, NESSIE, dedicated to the prediction of performances to accelerate the design of electronic embedded systems. <p><p>This tool has been developed in a previous thesis to cope with the limitations of existing design tools and offers a new solution to face the growing complexity of the current applications and electronic platforms and the multiple constraints they are subjected to. <p><p>More precisely, the goal of the tool is to propose a flexible framework targeting embedded systems in a generic way and enable a fast exploration of the design space based on the estimation of user-defined criteria and a joint hierarchical representation of the application and the platform.<p><p>In this context, the purpose of the thesis is to put the original framework NESSIE to the test to analyze if it is indeed useful and able to solve current design problems. Hence, the dissertation presents :<p><p>- A study of the State-of-the-Art related to the existing design tools. I propose a classification of these tools and compare them based on typical criteria. This substantial survey completes the State-of-the-Art done in the previous work. This study shows that the NESSIE framework offers solutions to the limitations of these tools.<p>- The framework of our original mapping tool and its calculation engine. Through this presentation, I highlight the main ingredients of the tool and explain the implemented methodology.<p>- Two external case studies that have been chosen to validate NESSIE and that are the core of the thesis. These case studies propose two different design problems (a reconfigurable processor, ADRES, applied to a matrix multiplication kernel and a 3D stacking MPSoC problem applied to a video decoder) and show the ability of our tool to target different applications and platforms. <p><p>The validation is performed based on the comparison of a multi-criteria estimation of the performances for a significant amount of solutions, between NESSIE and the external design flow. In particular, I discuss the prediction capability of NESSIE and the accuracy of the estimation. <p><p>-The study is completed, for each case study, by a quantification of the modeling time and the design time in both flows, in order to analyze the gain achieved by our tool used upstream from the classical tool chain compared to the existing design flow alone. <p><p><p>The results showed that NESSIE is able to predict with a high degree of accuracy the solutions that are the best candidates for the design in the lower design flows. Moreover, in both case studies, modeled respectively at a low and higher abstraction level, I obtained a significant gain in the design time. <p><p>However, I also identified limitations that impact the modeling time and could prevent an efficient use of the tool for more complex problems. <p><p>To cope with these issues, I end up by proposing several improvements of the framework and give perspectives to further develop the tool. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished