Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters

A study of the selection of microcomputer architectures to automate planetary spacecraft power systems

Performance and reliability models of alternate microcomputer architectures as a methodology for optimizing system design were examined. A methodology for selecting an optimum microcomputer architecture for autonomous operation of planetary spacecraft power systems was developed. Various microcomputer system architectures are analyzed to determine their application to spacecraft power systems. It is suggested that no standardization formula or common set of guidelines exists which provides an optimum configuration for a given set of specifications

Approximate Computing Strategies for Low-Overhead Fault Tolerance in Safety-Critical Applications

This work studies the reliability of embedded systems with approximate computing on software and hardware designs. It presents approximate computing methods and proposes approximate fault tolerance techniques applied to programmable hardware and embedded software to provide reliability at low computational costs. The objective of this thesis is the development of fault tolerance techniques based on approximate computing and proving that approximate computing can be applied to most safety-critical systems. It starts with an experimental analysis of the reliability of embedded systems used at safety-critical projects. Results show that the reliability of single-core systems, and types of errors they are sensitive to, differ from multicore processing systems. The usage of an operating system and two different parallel programming APIs are also evaluated. Fault injection experiment results show that embedded Linux has a critical impact on the system’s reliability and the types of errors to which it is most sensitive. Traditional fault tolerance techniques and parallel variants of them are evaluated for their fault-masking capability on multicore systems. The work shows that parallel fault tolerance can indeed not only improve execution time but also fault-masking. Lastly, an approximate parallel fault tolerance technique is proposed, where the system abandons faulty execution tasks. This first approximate computing approach to fault tolerance in parallel processing systems was able to improve the reliability and the fault-masking capability of the techniques, significantly reducing errors that would cause system crashes. Inspired by the conflict between the improvements provided by approximate computing and the safety-critical systems requirements, this work presents an analysis of the applicability of approximate computing techniques on critical systems. The proposed techniques are tested under simulation, emulation, and laser fault injection experiments. Results show that approximate computing algorithms do have a particular behavior, different from traditional algorithms. The approximation techniques presented and proposed in this work are also used to develop fault tolerance techniques. Results show that those new approximate fault tolerance techniques are less costly than traditional ones and able to achieve almost the same level of error masking.Este trabalho estuda a confiabilidade de sistemas embarcados com computação aproximada em software e projetos de hardware. Ele apresenta métodos de computação aproximada e técnicas aproximadas para tolerância a falhas em hardware programável e software embarcado que provêem alta confiabilidade a baixos custos computacionais. O objetivo desta tese é o desenvolvimento de técnicas de tolerância a falhas baseadas em computação aproximada e provar que este paradigma pode ser usado em sistemas críticos. O texto começa com uma análise da confiabilidade de sistemas embarcados usados em sistemas de tolerância crítica. Os resultados mostram que a resiliência de sistemas singlecore, e os tipos de erros aos quais eles são mais sensíveis, é diferente dos multi-core. O uso de sistemas operacionais também é analisado, assim como duas APIs de programação paralela. Experimentos de injeção de falhas mostram que o uso de Linux embarcado tem um forte impacto na confiabilidade do sistema. Técnicas tradicionais de tolerância a falhas e variações paralelas das mesmas são avaliadas. O trabalho mostra que técnicas de tolerância a falhas paralelas podem de fato melhorar não apenas o tempo de execução da aplicação, mas também seu mascaramento de erros. Por fim, uma técnica de tolerância a falhas paralela aproximada é proposta, onde o sistema abandona instâncias de execuções que apresentam falhas. Esta primeira experiência com computação aproximada foi capaz de melhorar a confiabilidade das técnicas previamente apresentadas, reduzindo significativamente a ocorrência de erros que provocam um crash total do sistema. Inspirado pelo conflito entre as melhorias trazidas pela computação aproximada e os requisitos dos sistemas críticos, este trabalho apresenta uma análise da aplicabilidade de computação aproximada nestes sistemas. As técnicas propostas são testadas sob experimentos de injeção de falhas por simulação, emulação e laser. Os resultados destes experimentos mostram que algoritmos aproximados possuem um comportamento particular que lhes é inerente, diferente dos tradicionais. As técnicas de aproximação apresentadas e propostas no trabalho são também utilizadas para o desenvolvimento de técnicas de tolerância a falhas aproximadas. Estas novas técnicas possuem um custo menor que as tradicionais e são capazes de atingir o mesmo nível de mascaramento de erros

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified