Observation mechanisms for in-field software-based self-test

When electronic systems are used in safety critical applications, as in the space, avionic, automotive or biomedical areas, it is required to maintain a very low probability of failures due to faults of any kind. Standards and regulations play a significant role, forcing companies to devise and adopt solutions able to achieve predefined targets in terms of dependability. Different techniques can be used to reduce fault occurrence or to minimize the probability that those faults produce critical failures (e.g., by introducing redundancy). Unfortunately, most of these techniques have a severe impact on the cost of the resulting product and, in some cases, the probability of failures is too large anyway. Hence, a solution commonly used in several scenarios lies on periodically performing a test able to detect the occurrence of any fault before it produces a failure (in-field test). This solution is normally based on forcing the processor inside the Device Under Test to execute a properly written test program, which is able to activate possible faults and to make their effects visible in some observable locations. This approach is also called Software-Based Self-Test, or SBST. If compared with testing in an end of manufacturing scenario, in-field testing has strong limitations in terms of access to the system inputs and outputs because Design for Testability structures and testing equipment are usually not available. As a consequence there are reduced possibilities to activate the faults and to observe their effects. This reduced observability particularly affects the ability to detect performance faults, i.e. faults that modify the timing but not the final value of computations. This kind of faults are hard to detect by only observing the final content of predefined memory locations, that is the usual test result observation method used in-field. Initially, the present work was focused on fault tolerance techniques against transient faults induced by ionizing radiation, the so called Single Event Upsets (SEUs). The main contribution of this early stage of the thesis lies in the experimental validation of the feasibility of achieving a safe system by using an architecture that combines task-level redundancy with already available IP cores, thus minimizing the development time. Task execution is replicated and Memory Protection is used to guarantee that any SEU may affect one and only one of the replicas. A proof of concept implementation was developed and validated using fault injection. Results outline the effectiveness of the architecture, and the overhead analysis shows that the proposed architecture is effective in reducing the resource occupation with respect to N-modular redundancy, at an affordable cost in terms of application execution time. The main part of the thesis is focused on in-field software-based self-test of permanent faults. A set of observation methods exploiting existing or ad-hoc hardware is proposed, aimed at obtaining a better coverage, in particular of performance faults. An extensive quantitative evaluation of the proposed methods is presented, including a comparison with the observation methods traditionally used in end of manufacturing and in-field testing. Results show that the proposed methods are a good complement to the traditionally used final memory content observation. Moreover, they show that an adequate combination of these complementary methods allows for achieving nearly the same fault coverage achieved when continuously observing all the processor outputs, which is an observation method commonly used for production test but usually not available in-field. A very interesting by-product of what is described above is a detailed description of how to compute the fault coverage achieved by functional in-field tests using a conventional fault simulator, a tool that is usually applied in an end of manufacturing testing scenario. Finally, another relevant result in the testing area is a method to detect permanent faults inside the cache coherence logic integrated in each cache controller of a multi-core system, based on the concurrent execution of a test program by the different cores in a coordinated manner. By construction, the method achieves full fault coverage of the static faults in the addressed logic.Cuando se utilizan sistemas electrónicos en aplicaciones críticas como en las áreas biomédica, aeroespacial o automotriz, se requiere mantener una muy baja probabilidad de malfuncionamientos debidos a cualquier tipo de fallas. Los estándares y normas juegan un papel importante, forzando a los desarrolladores a diseñar y adoptar soluciones que sean capaces de alcanzar objetivos predefinidos en cuanto a seguridad y confiabilidad. Pueden utilizarse diferentes técnicas para reducir la ocurrencia de fallas o para minimizar la probabilidad de que esas fallas produzcan mal funcionamientos críticos, por ejemplo a través de la incorporación de redundancia. Lamentablemente, muchas de esas técnicas afectan en gran medida el costo de los productos y, en algunos casos, la probabilidad de malfuncionamiento sigue siendo demasiado alta. En consecuencia, una solución usada a menudo en varios escenarios consiste en realizar periódicamente un test que sea capaz de detectar la ocurrencia de una falla antes de que esta produzca un mal funcionamiento (test en campo). En general, esta solución se basa en forzar a un procesador existente dentro del dispositivo bajo prueba a ejecutar un programa de test que sea capaz de activar las posibles fallas y de hacer que sus efectos sean visibles en puntos observables. A esta metodología también se la llama auto-test basado en software, o en inglés Software-Based Self-Test (SBST). Si se lo compara con un escenario de test de fin de fabricación, el test en campo tiene fuertes limitaciones en términos de posibilidad de acceso a las entradas y salidas del sistema, porque usualmente no se dispone de equipamiento de test ni de la infraestructura de Design for Testability. En consecuencia se tiene menos posibilidades de activar las fallas y de observar sus efectos. Esta observabilidad reducida afecta particularmente la habilidad para detectar fallas de performance, es decir fallas que modifican la temporización pero no el resultado final de los cálculos. Este tipo de fallas es difícil de detectar por la sola observación del contenido final de lugares de memoria, que es el método usual que se utiliza para observar los resultados de un test en campo. Inicialmente, el presente trabajo estuvo enfocado en técnicas para tolerar fallas transitorias inducidas por radiación ionizante, llamadas en inglés Single Event Upsets (SEUs). La principal contribución de esa etapa inicial de la tesis reside en la validación experimental de la viabilidad de obtener un sistema seguro, utilizando una arquitectura que combina redundancia a nivel de tareas con el uso de módulos hardware (IP cores) ya disponibles, que minimiza en consecuencia el tiempo de desarrollo. Se replica la ejecución de las tareas y se utiliza protección de memoria para garantizar que un SEU pueda afectar a lo sumo a una sola de las réplicas. Se desarrolló una implementación para prueba de concepto que fue validada mediante inyección de fallas. Los resultados muestran la efectividad de la arquitectura, y el análisis de los recursos utilizados muestra que la arquitectura propuesta es efectiva en reducir la ocupación con respecto a la redundancia modular con N réplicas, a un costo accesible en términos de tiempo de ejecución. La parte principal de esta tesis se enfoca en el área de auto-test en campo basado en software para la detección de fallas permanentes. Se propone un conjunto de métodos de observación utilizando hardware existente o ad-hoc, con el fin de obtener una mejor cobertura, en particular de las fallas de performance. Se presenta una extensa evaluación cuantitativa de los métodos propuestos, que incluye una comparación con los métodos tradicionalmente utilizados en tests de fin de fabricación y en campo. Los resultados muestran que los métodos propuestos son un buen complemento del método tradicionalmente usado que consiste en observar el valor final del contenido de memoria. Además muestran que una adecuada combinación de estos métodos complementarios permite alcanzar casi los mismos valores de cobertura de fallas que se obtienen mediante la observación continua de todas las salidas del procesador, método comúnmente usado en tests de fin de fabricación, pero que usualmente no está disponible en campo. Un subproducto muy interesante de lo arriba expuesto es la descripción detallada del procedimiento para calcular la cobertura de fallas lograda mediante tests funcionales en campo por medio de un simulador de fallas convencional, una herramienta que usualmente se aplica en escenarios de test de fin de fabricación. Finalmente, otro resultado relevante en el área de test es un método para detectar fallas permanentes dentro de la lógica de coherencia de cache que está integrada en el controlador de cache de cada procesador en un sistema multi procesador. El método está basado en la ejecución de un programa de test en forma coordinada por parte de los diferentes procesadores. Por construcción, el método cubre completamente las fallas de la lógica mencionad

Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146

Model-based design, analysis and synthesis for multi-core and TSP avionics targets

Multi-core, and Time and Space Partitionnong sys- tems are two emerging paradigms for architecting avionics systems. They impose new steps in the development process: capturing configuration attributes, analysing their correctness, or guaranteeing performance. In this context, model-based tech- niques provide a framework to design, analyse and synthesize these systems while automating much steps. In this paper, we report on a set of extenstions of TASTE to support multi-core and TSP systems. We first present the key architectural elements of these systems, and then detail how these have been support as part of the generation toolchain. We then present experiments realized on two case studies and two hardware targets, both provided with the XtratuM hypervisor

Real-Time Trace Decoding and Monitoring for Safety and Security in Embedded Systems

Integrated circuits and systems can be found almost everywhere in today’s world. As their use increases, they need to be made safer and more perfor mant to meet current demands in processing power. FPGA integrated SoCs can provide the ideal trade-off between performance, adaptability, and energy usage. One of today’s vital challenges lies in updating existing fault tolerance techniques for these new systems while utilizing all available processing capa bilities, such as multi-core and heterogeneous processing units. Control-flow monitoring is one of the primary mechanisms described for error detection at the software architectural level for the highest grade of hazard level clas sifications (e.g., ASIL D) described in industry safety standards ISO-26262. Control-flow errors are also known to compose the majority of detected errors for ICs and embedded systems in safety-critical and risk-susceptible environ ments [5]. Software-based monitoring methods remain the most popular [6–8]. However, recent studies show that the overheads they impose make actual reliability gains negligible [9, 10]. This work proposes and demonstrates a new control flow checking method implemented in FPGA for multi-core embedded systems called control-flow trace checker (CFTC). CFTC uses existing trace and debug subsystems of modern processors to rebuild their execution states. It can iden tify any errors in real-time by comparing executed states to a set of permitted state transitions determined statically. This novel implementation weighs hardware resource trade-offs to target mul tiple independent tasks in multi-core embedded applications, as well as single core systems. The proposed system is entirely implemented in hardware and isolated from all monitored software components, requiring 2.4% of the target FPGA platform resources to protect an execution unit in its entirety. There fore, it avoids undesired overheads and maintains deterministic error detection latencies, which guarantees reliability improvements without impairing the target software system. Finally, CFTC is evaluated under different software i Resumo fault-injection scenarios, achieving detection rates of 100% of all control-flow errors to wrong destinations and 98% of all injected faults to program binaries. All detection times are further analyzed and precisely described by a model based on the monitor’s resources and speed and the software application’s control-flow structure and binary characteristics.Circuitos integrados estão presentes em quase todos sistemas complexos do mundo moderno. Conforme sua frequência de uso aumenta, eles precisam se tornar mais seguros e performantes para conseguir atender as novas demandas em potência de processamento. Sistemas em Chip integrados com FPGAs conseguem prover o balanço perfeito entre desempenho, adaptabilidade, e uso de energia. Um dos maiores desafios agora é a necessidade de atualizar técnicas de tolerância à falhas para estes novos sistemas, aproveitando os novos avanços em capacidade de processamento. Monitoramento de fluxo de controle é um dos principais mecanismos para a detecção de erros em nível de software para sistemas classificados como de alto risco (e.g. ASIL D), descrito em padrões de segurança como o ISO-26262. Estes erros são conhecidos por compor a maioria dos erros detectados em sistemas integrados [5]. Embora métodos de monitoramento baseados em software continuem sendo os mais populares [6–8], estudos recentes mostram que seus custos adicionais, em termos de performance e área, diminuem consideravelmente seus ganhos reais em confiabilidade [9, 10]. Propomos aqui um novo método de monitora mento de fluxo de controle implementado em FPGA para sistemas embarcados multi-core. Este método usa subsistemas de trace e execução de código para reconstruir o estado atual do processador, identificando erros através de com parações entre diferentes estados de execução da CPU. Propomos uma implementação que considera trade-offs no uso de recuros de sistema para monitorar múltiplas tarefas independetes. Nossa abordagem suporta o monitoramento de sistemas simples e também de sistemas multi-core multitarefa. Por fim, nossa técnica é totalmente implementada em hardware, evitando o uso de unidades de processamento de software que possa adicionar custos indesejáveis à aplicação em perda de confiabilidade. Propomos, assim, um mecanismo de verificação de fluxo de controle, escalável e extensível, para proteção de sistemas embarcados críticos e multi-core

Real-time trace decoding and monitoring for safety and security in embedded systems

Integrated circuits and systems can be found almost everywhere in today’s world. As their use increases, they need to be made safer and more perfor mant to meet current demands in processing power. FPGA integrated SoCs can provide the ideal trade-off between performance, adaptability, and energy usage. One of today’s vital challenges lies in updating existing fault tolerance techniques for these new systems while utilizing all available processing capa bilities, such as multi-core and heterogeneous processing units. Control-flow monitoring is one of the primary mechanisms described for error detection at the software architectural level for the highest grade of hazard level clas sifications (e.g., ASIL D) described in industry safety standards ISO-26262. Control-flow errors are also known to compose the majority of detected errors for ICs and embedded systems in safety-critical and risk-susceptible environ ments [5]. Software-based monitoring methods remain the most popular [6–8]. However, recent studies show that the overheads they impose make actual reliability gains negligible [9, 10]. This work proposes and demonstrates a new control flow checking method implemented in FPGA for multi-core embedded systems called control-flow trace checker (CFTC). CFTC uses existing trace and debug subsystems of modern processors to rebuild their execution states. It can iden tify any errors in real-time by comparing executed states to a set of permitted state transitions determined statically. This novel implementation weighs hardware resource trade-offs to target mul tiple independent tasks in multi-core embedded applications, as well as single core systems. The proposed system is entirely implemented in hardware and isolated from all monitored software components, requiring 2.4% of the target FPGA platform resources to protect an execution unit in its entirety. There fore, it avoids undesired overheads and maintains deterministic error detection latencies, which guarantees reliability improvements without impairing the target software system. Finally, CFTC is evaluated under different software i Resumo fault-injection scenarios, achieving detection rates of 100% of all control-flow errors to wrong destinations and 98% of all injected faults to program binaries. All detection times are further analyzed and precisely described by a model based on the monitor’s resources and speed and the software application’s control-flow structure and binary characteristics.Circuitos integrados estão presentes em quase todos sistemas complexos do mundo moderno. Conforme sua frequência de uso aumenta, eles precisam se tornar mais seguros e performantes para conseguir atender as novas demandas em potência de processamento. Sistemas em Chip integrados com FPGAs conseguem prover o balanço perfeito entre desempenho, adaptabilidade, e uso de energia. Um dos maiores desafios agora é a necessidade de atualizar técnicas de tolerância à falhas para estes novos sistemas, aproveitando os novos avanços em capacidade de processamento. Monitoramento de fluxo de controle é um dos principais mecanismos para a detecção de erros em nível de software para sistemas classificados como de alto risco (e.g. ASIL D), descrito em padrões de segurança como o ISO-26262. Estes erros são conhecidos por compor a maioria dos erros detectados em sistemas integrados [5]. Embora métodos de monitoramento baseados em software continuem sendo os mais populares [6–8], estudos recentes mostram que seus custos adicionais, em termos de performance e área, diminuem consideravelmente seus ganhos reais em confiabilidade [9, 10]. Propomos aqui um novo método de monitora mento de fluxo de controle implementado em FPGA para sistemas embarcados multi-core. Este método usa subsistemas de trace e execução de código para reconstruir o estado atual do processador, identificando erros através de com parações entre diferentes estados de execução da CPU. Propomos uma implementação que considera trade-offs no uso de recuros de sistema para monitorar múltiplas tarefas independetes. Nossa abordagem suporta o monitoramento de sistemas simples e também de sistemas multi-core multitarefa. Por fim, nossa técnica é totalmente implementada em hardware, evitando o uso de unidades de processamento de software que possa adicionar custos indesejáveis à aplicação em perda de confiabilidade. Propomos, assim, um mecanismo de verificação de fluxo de controle, escalável e extensível, para proteção de sistemas embarcados críticos e multi-core

Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application

New challenges in Astronomy and Astrophysics (AA) are urging the need for a large number of exceptionally computationally intensive simulations. "Exascale" (and beyond) computational facilities are mandatory to address the size of theoretical problems and data coming from the new generation of observational facilities in AA. Currently, the High Performance Computing (HPC) sector is undergoing a profound phase of innovation, in which the primary challenge to the achievement of the "Exascale" is the power-consumption. The goal of this work is to give some insights about performance and energy footprint of contemporary architectures for a real astrophysical application in an HPC context. We use a state-of-the-art N-body application that we re-engineered and optimized to exploit the heterogeneous underlying hardware fully. We quantitatively evaluate the impact of computation on energy consumption when running on four different platforms. Two of them represent the current HPC systems (Intel-based and equipped with NVIDIA GPUs), one is a micro-cluster based on ARM-MPSoC, and one is a "prototype towards Exascale" equipped with ARM-MPSoCs tightly coupled with FPGAs. We investigate the behavior of the different devices where the high-end GPUs excel in terms of time-to-solution while MPSoC-FPGA systems outperform GPUs in power consumption. Our experience reveals that considering FPGAs for computationally intensive application seems very promising, as their performance is improving to meet the requirements of scientific applications. This work can be a reference for future platforms development for astrophysics applications where computationally intensive calculations are required.Comment: 15 pages, 4 figures, 3 tables; Preprint (V2) submitted to MDPI (Special Issue: Energy-Efficient Computing on Parallel Architectures