As reconfigurable devices' capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART's availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to "five nines" (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability.

[pt] Elementos sensores baseados no efeito GMI são uma nova família de sensores magnéticos que apresentam grande quando submetidos a campos magnéticos externos. Estes sensores têm sido utilizados no desenvolvimento de magnetômetros de alta sensibilidade, destinados à medição de campos ultra fracos. Por sua vez, a sensibilidade de um magnetômetro está diretamente associada à sensibilidade de seus elementos sensores. No caso de amostras GMI, esta sensibilidade é otimizada buscando-se a maximização da variação do módulo ou da fase da impedância em função do campo magnético ao qual a amostra é submetida. Estudos recentes mostram que transdutores GMI baseados na variação de fase podem exibir sensibilidades até 100 vezes superiores às apresentadas por transdutores baseados na leitura do módulo do elemento sensor, o que fez com que os trabalhos conduzidos nesta dissertação focassem na maximização da sensibilidade de fase, a qual é majoritariamente dependente de quatro fatores: o comprimento da amostra, o campo magnético externo, o nível DC e a frequência da corrente de excitação. Contudo, a busca do conjunto de parâmetros que otimiza a sensibilidade das amostras é geralmente empírica e muito demorada. Esta dissertação propõe uma nova técnica de otimização da sensibilidade, baseada no uso de algoritmos genéticos evoluindo em hardware, a fim de se definir qual o conjunto de parâmetros responsável pela maximização da sensibilidade das amostras. Ressalta-se que, além dos parâmetros de otimização anteriormente explicitados, também foram realizados testes considerando a amplitude da corrente de excitação como uma variável livre, sendo que os resultados obtidos são apresentados e discutidos. Foi implementada uma bancada de testes e desenvolvida uma interface gráfica em LabVIEW, para monitorar e medir o comportamento da impedância de amostras GMI em função de variações nos parâmetros de interesse. Por sua vez, implementou-se um módulo de otimização em Matlab, baseado em algoritmos genéticos, responsável por encontrar a combinação de parâmetros que maximiza a sensibilidade dos sensores GMI avaliados (ponto ótimo de operação). / [en] GMI sensors are a new family of magnetic sensors that exhibit a huge variation of their impedance when subjected to external magnetic fields. These sensors have been used in the development of high sensitivity magnetometers, aimed at measuring ultra-weak magnetic fields. In turn, the sensitivity of a magnetometer is directly associated with the sensitivity of their sensor elements. In the case of GMI samples, this sensitivity is optimized by maximizing the variation of the impedance magnitude or phase as a function of the magnetic field applied to the sample. Recent studies show that GMI transducers based on phase variation can exhibit sensitivities up to 100 times higher than those presented by transducers based on impedance magnitude readings. The results obtained in these previous studies made the current work focusing on the maximization of phase sensitivity, which is mostly dependent on four factors: sample length, external magnetic field, DC level and frequency of the excitation current. However, the search for the set of parameters that optimizes the sensitivity of the samples is usually empirical and very time consuming. Thus, this dissertation proposes a new optimization technique, based on the use of genetic algorithms evolving on hardware, in order to define which set of parameters is responsible for maximizing the sensitivity of the samples. It should be noted that in addition to the optimization parameters previously described, this work also carried out tests considering the amplitude of the excitation current as a free variable, and the results obtained are presented and discussed. A test bench was implemented and a graphical interface was developed in LabVIEW to monitor and measure the impedance behavior of GMI samples due to variations in the parameters of interest. In turn, a Matlab optimization module based on genetic algorithms was implemented, in order to find the combination of parameters that maximizes the impedance phase sensitivity of the evaluated GMI sensors (optimum operating point).

Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-03T18:47:50Z No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T18:27:58Z (GMT) No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T18:28:04Z (GMT) No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Made available in DSpace on 2016-10-20T18:28:13Z (GMT). No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) Previous issue date: 2016-03-04 / Não recebi financiamento / The use of technology based on Field Programmable Gate Arrays (FPGAs), a reconfigurable technology, has become a frequent object of study. This technique is feasible and a promising application in the development of embedded systems, however, the difficulty in finding a flexible and efficient way to perform such an application is their bigger problem. In this work, a virtual and reconfigurable architecture (AVR) in FPGA for hardware applications is presented using a Genetic Programming Software on the development of an optimal reconfiguration for this AVR, in order to build a hardware capable of performing a given task in an embedded system. This proposal is a simple, flexible and efficient way to achieve appropriate applications in embedded systems, when compared to other reconfigurable hardware techniques. The representation of phenotype of the proposed evolutionary system is based on a bi-dimensional network function elements (EF). The GPLAB tool for MATLAB is used in Genetic Programming, and the solution found by this procedure is converted into a memory mapping to represent the best solution, where it is used to reconfigure the hardware. In the tests, GPLAB found results for logic circuits in a few generations, and for image filters containing efficient solutions, where there was little hardware occupation, especially memory, in the cases this has been presented, with a reduced chromosome size, shows a proposal efficiency. / O uso da tecnologia baseada em Field Programmable Gate Arrays (FPGAs), de forma reconfigurável, para a solução de diversos problemas atuais, tem se tornado um frequente objeto de estudo. Essa técnica é de aplicação viável e promissora na elaboração de sistemas embarcados, porém, a dificuldade em encontrar uma forma flexível e eficiente de realizar tal aplicação é o seu maior problema. Neste trabalho, é apresentada uma arquitetura virtual e reconfigurável (AVR) em FPGA para aplicações em hardware, utilizando um software de Programação Genética na elaboração de uma reconfiguração ótima para esta AVR, de forma a construir um hardware capaz de efetuar uma determinada tarefa em um sistema embarcado. Esta proposta é uma forma simples, flexível e eficiente de realizar aplicações adequadas em sistemas embarcados, quando comparada a outras técnicas de hardware reconfigurável. A representação do fenótipo no sistema evolutivo proposto se baseia em uma rede de elementos de função (EF) bidimensional. A ferramenta GPLAB, para MATLAB, é usada na Programação Genética, e a solução encontrada por esta é convertida em um mapeamento de memória com o cromossomo da melhor solução, onde este é usado para reconfigurar o hardware. Nos testes realizados, a GPLAB encontrou resultados para circuitos lógicos em poucas gerações, e para filtros de imagem encontrou soluções eficientes, onde ocorreu pouca ocupação de hardware, principalmente da memória nos casos apresentados, apresentando um cromossomo de tamanho reduzido, o que demonstra uma boa eficiência da proposta.

This diploma thesis deals with the use of Cartesian Genetic Programming (CGP) for combinational circuits design. The work addresses the issue of optimizaion of selected logic circuts, arithmetic adders and multipliers, using Cartesian Genetic Programming. The implementation of the CPG is performed in the Python programming language with the aid of NumPy, Numba and Pandas libraries. The method was tested on selected examples and the results were discussed.