Proposta de um processador multithreading com características de previsibilidade

The real-time embedded systems design requires precise control of the passage of time in the computation performed by the modules and communication between them. Generally, these systems consist of several modules, each designed for a specific task and restricted communication with other modules in order to obtain the required timing. This strategy, called federated architecture, is already becoming unviable in front of the current demands of cost, required performance and quality of embedded system. To address this problem, it has been proposed the use of integrated architectures that consist of one or few circuits performing multiple tasks in parallel in a more efficient manner and with reduced costs. However, one has to ensure that the integrated architecture has temporal composability, ie the ability to design each task temporally isolated from the others in order to maintain the individual characteristics of each task. The Precision Timed Machines are an integrated architecture approach that makes use of multithreaded processors to ensure temporal composability. Thus, this work presents the implementation of a Precision Machine Timed named Hivek-RT. This processor which is a VLIW supporting Simultaneous Multithreading is capable of efficiently execute real-time tasks when compared to a traditional processor. In addition to the efficient implementation, the proposed architecture facilitates the implementation real-time tasks from a programming point of view.

Máquina de vetores-suporte intervalar

The Support Vector Machines (SVM) has attracted increasing attention in machine learning area, particularly on classification and patterns recognition. However, in some cases it is not easy to determinate accurately the class which given pattern belongs. This thesis involves the construction of a intervalar pattern classifier using SVM in association with intervalar theory, in order to model the separation of a pattern set between distinct classes with precision, aiming to obtain an optimized separation capable to treat imprecisions contained in the initial data and generated during the computational processing. The SVM is a linear machine. In order to allow it to solve real-world problems (usually nonlinear problems), it is necessary to treat the pattern set, know as input set, transforming from nonlinear nature to linear problem. The kernel machines are responsible to do this mapping. To create the intervalar extension of SVM, both for linear and nonlinear problems, it was necessary define intervalar kernel and the Mercer s theorem (which caracterize a kernel function) to intervalar function