Analyse und Erweiterung eines fehler-toleranten NoC für SRAM-basierte FPGAs in Weltraumapplikationen

Data Processing Units for scientific space mission need to process ever higher volumes of data and perform ever complex calculations. But the performance of available space-qualified general purpose processors is just in the lower three digit megahertz range, which is already insufficient for some applications. As an alternative, suitable processing steps can be implemented in hardware on a space-qualified SRAM-based FPGA. However, suitable devices are susceptible against space radiation. At the Institute for Communication and Network Engineering a fault-tolerant, network-based communication architecture was developed, which enables the construction of processing chains on the basis of different processing modules within suitable SRAM-based FPGAs and allows the exchange of single processing modules during runtime, too. The communication architecture and its protocol shall isolate non SEU mitigated or just partial SEU mitigated modules affected by radiation-induced faults to prohibit the propagation of errors within the remaining System-on-Chip. In the context of an ESA study, this communication architecture was extended with further components and implemented in a representative hardware platform. Based on the acquired experiences during the study, this work analyses the actual fault-tolerance characteristics as well as weak points of this initial implementation. At appropriate locations, the communication architecture was extended with mechanisms for fault-detection and fault-differentiation as well as with a hardware-based monitoring solution. Both, the former measures and the extension of the employed hardware-platform with selective fault-injection capabilities for the emulation of radiation-induced faults within critical areas of a non SEU mitigated processing module, are used to evaluate the effects of radiation-induced faults within the communication architecture. By means of the gathered results, further measures to increase fast detection and isolation of faulty nodes are developed, selectively implemented and verified. In particular, the ability of the communication architecture to isolate network nodes without SEU mitigation could be significantly improved.Instrumentenrechner für wissenschaftliche Weltraummissionen müssen ein immer höheres Datenvolumen verarbeiten und immer komplexere Berechnungen ausführen. Die Performanz von verfügbaren qualifizierten Universalprozessoren liegt aber lediglich im unteren dreistelligen Megahertz-Bereich, was für einige Anwendungen bereits nicht mehr ausreicht. Als Alternative bietet sich die Implementierung von entsprechend geeigneten Datenverarbeitungsschritten in Hardware auf einem qualifizierten SRAM-basierten FPGA an. Geeignete Bausteine sind jedoch empfindlich gegenüber der Strahlungsumgebung im Weltraum. Am Institut für Datentechnik und Kommunikationsnetze wurde eine fehlertolerante netzwerk-basierte Kommunikationsarchitektur entwickelt, die innerhalb eines geeigneten SRAM-basierten FPGAs Datenverarbeitungsmodule miteinander nach Bedarf zu Verarbeitungsketten verbindet, sowie den Austausch von einzelnen Modulen im Betrieb ermöglicht. Nicht oder nur partiell SEU mitigierte Module sollen bei strahlungsbedingten Fehlern im Modul durch das Protokoll und die Fehlererkennungsmechanismen der Kommunikationsarchitektur isoliert werden, um ein Ausbreiten des Fehlers im restlichen System-on-Chip zu verhindern. Im Kontext einer ESA Studie wurde diese Kommunikationsarchitektur um Komponenten erweitert und auf einer repräsentativen Hardwareplattform umgesetzt. Basierend auf den gesammelten Erfahrungen aus der Studie, wird in dieser Arbeit eine Analyse der tatsächlichen Fehlertoleranz-Eigenschaften sowie der Schwachstellen dieser ursprünglichen Implementierung durchgeführt. Die Kommunikationsarchitektur wurde an geeigneten Stellen um Fehlerdetektierungs- und Fehlerunterscheidungsmöglichkeiten erweitert, sowie um eine hardwarebasierte Überwachung ergänzt. Sowohl diese Maßnahmen, als auch die Erweiterung der Hardwareplattform um gezielte Fehlerinjektions-Möglichkeiten zum Emulieren von strahlungsinduzierten Fehlern in kritischen Komponenten eines nicht SEU mitigierten Prozessierungsmoduls werden genutzt, um die tatsächlichen auftretenden Effekte in der Kommunikationsarchitektur zu evaluieren. Anhand der Ergebnisse werden weitere Verbesserungsmaßnahmen speziell zur schnellen Detektierung und Isolation von fehlerhaften Knoten erarbeitet, selektiv implementiert und verifiziert. Insbesondere die Fähigkeit, fehlerhafte, nicht SEU mitigierte Netzwerkknoten innerhalb der Kommunikationsarchitektur zu isolieren, konnte dabei deutlich verbessert werden

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Survey of Soft Error Mitigation Techniques Applied to LEON3 Soft Processors on SRAM-Based FPGAs

Soft-core processors implemented in SRAM-based FPGAs are an attractive option for applications to be employed in radiation environments due to their flexibility, relatively-low application development costs, and reconfigurability features enabling them to adapt to the evolving mission needs. Despite the advantages soft-core processors possess, they are seldom used in critical applications because they are more sensitive to radiation than their hard-core counterparts. For instance, both the logic and signal routing circuitry of a soft-core processor as well as its user memory are susceptible to radiation-induced faults. Therefore, soft-core processors must be appropriately hardened against ionizing-radiation to become a feasible design choice for harsh environments and thus to reap all their benefits. This survey henceforth discusses various techniques to protect the configuration and user memories of an LEON3 soft processor, which is one of the most widely used soft-core processors in radiation environments, as reported in the state-of-the-art literature, with the objective of facilitating the choice of right fault-mitigation solution for any given soft-core processor

FPGA dynamic and partial reconfiguration : a survey of architectures, methods, and applications

Dynamic and partial reconfiguration are key differentiating capabilities of field programmable gate arrays (FPGAs). While they have been studied extensively in academic literature, they find limited use in deployed systems. We review FPGA reconfiguration, looking at architectures built for the purpose, and the properties of modern commercial architectures. We then investigate design flows, and identify the key challenges in making reconfigurable FPGA systems easier to design. Finally, we look at applications where reconfiguration has found use, as well as proposing new areas where this capability places FPGAs in a unique position for adoption

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 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\u27s 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

Radiation Mitigation and Power Optimization Design Tools for Reconfigurable Hardware in Orbit

The Reconfigurable Hardware in Orbit (RHinO)project is focused on creating a set of design tools that facilitate and automate design techniques for reconfigurable computing in space, using SRAM-based field-programmable-gate-array (FPGA) technology. In the second year of the project, design tools that leverage an established FPGA design environment have been created to visualize and analyze an FPGA circuit for radiation weaknesses and power inefficiencies. For radiation, a single event Upset (SEU) emulator, persistence analysis tool, and a half-latch removal tool for Xilinx/Virtex-II devices have been created. Research is underway on a persistence mitigation tool and multiple bit upsets (MBU) studies. For power, synthesis level dynamic power visualization and analysis tools have been completed. Power optimization tools are under development and preliminary test results are positive

Zero-maintenance of electronic systems: Perspectives, challenges, and opportunities

Self-engineering systems that are capable of repairing themselves in-situ without the need for human decision (or intervention) could be used to achieve zero-maintenance. This philosophy is synonymous to the way in which the human body heals and repairs itself up to a point. This article synthesises issues related to an emerging area of self-healing technologies that links software and hardware mitigations strategies. Efforts are concentrated on built-in detection, masking and active mitigation that comprises self-recovery or self-repair capability, and has a focus on system resilience and recovering from fault events. Design techniques are critically reviewed to clarify the role of fault coverage, resource allocation and fault awareness, set in the context of existing and emerging printable/nanoscale manufacturing processes. The qualitative analysis presents new opportunities to form a view on the research required for a successful integration of zero-maintenance. Finally, the potential cost benefits and future trends are enumerated

On the Reliability of Neural Networks Implemented on SRAM-based FPGAs for Low-cost Satellites

Recent development in the neural network inference frameworks on Field-Programmable Gate Arrays (FPGAs) enables the rapid deployment of neural network applications on low-power FPGA devices. FPGAs are a promising platform for implementing neural network capabilities on board satellites thanks to the high energy efficiency of quantised neural networks on FPGAs. Furthermore, the reconfigurability of FPGAs allows neural network accelerators to share the FPGA with other onboard computer systems for reduced hardware complexity. However, the reliability against radiation-induced upsets of existing neural network inference frameworks on commercial FPGA devices was not previously studied. The reliability of neural network applications on FPGA is complicated by the perceptrons’ inherent algorithm-based fault tolerance, quantisation techniques, the varying sensitivity of non-neural layers like pooling layers, the architecture of the accelerator, and the software stack. This thesis explores the effect of single event upsets (SEUs) in potential spaceborne FPGA-based neural network applications using fully connected and convolutional networks, on applications using binary, 4-bit and 8-bit quantisation levels, and on applications created from both FINN and Vitis AI frameworks. We study the failure modes in neural network applications caused by SEUs, including loss of accuracy, reduction of throughput/timeout, and catastrophic system failure on FPGA SoC. We conducted fault injection experiments on fully connected and convolutional neural networks (CNNs) trained for classifying images from the MNIST handwritten digits dataset and the Airbus ship detection dataset. We found that SEUs have an insignificant impact on fully-connected binary networks trained on the MNIST dataset. However, the more complex CNN applications created from the FINN and Vitis-AI frameworks showed much higher sensitivity to SEUs and had more failure modes, including loss of accuracy, hardware hang-up, and even catastrophic failure in the OS of SoC devices due to erroneous driver behaviour. We found that the SEU cross-section of model-specific neural network accelerators like FINN can be reduced significantly by quantising the network to a lower precision. We also studied the efficacy of fault-tolerant design techniques, including full TMR and partial TMR, on the binary neural network and FINN accelerator

Dynamic partial reconfiguration management for high performance and reliability in FPGAs

Modern Field-Programmable Gate Arrays (FPGAs) are no longer used to implement small “glue logic” circuitries. The high-density of reconfigurable logic resources in today’s FPGAs enable the implementation of large systems in a single chip. FPGAs are highly flexible devices; their functionality can be altered by simply loading a new binary file in their configuration memory. While the flexibility of FPGAs is comparable to General-Purpose Processors (GPPs), in the sense that different functions can be performed using the same hardware, the performance gain that can be achieved using FPGAs can be orders of magnitudes higher as FPGAs offer the ability for customisation of parallel computational architectures. Dynamic Partial Reconfiguration (DPR) allows for changing the functionality of certain blocks on the chip while the rest of the FPGA is operational. DPR has sparked the interest of researchers to explore new computational platforms where computational tasks are off-loaded from a main CPU to be executed using dedicated reconfigurable hardware accelerators configured on demand at run-time. By having a battery of custom accelerators which can be swapped in and out of the FPGA at runtime, a higher computational density can be achieved compared to static systems where the accelerators are bound to fixed locations within the chip. Furthermore, the ability of relocating these accelerators across several locations on the chip allows for the implementation of adaptive systems which can mitigate emerging faults in the FPGA chip when operating in harsh environments. By porting the appropriate fault mitigation techniques in such computational platforms, the advantages of FPGAs can be harnessed in different applications in space and military electronics where FPGAs are usually seen as unreliable devices due to their sensitivity to radiation and extreme environmental conditions. In light of the above, this thesis investigates the deployment of DPR as: 1) a method for enhancing performance by efficient exploitation of the FPGA resources, and 2) a method for enhancing the reliability of systems intended to operate in harsh environments. Achieving optimal performance in such systems requires an efficient internal configuration management system to manage the reconfiguration and execution of the reconfigurable modules in the FPGA. In addition, the system needs to support “fault-resilience” features by integrating parameterisable fault detection and recovery capabilities to meet the reliability standard of fault-tolerant applications. This thesis addresses all the design and implementation aspects of an Internal Configuration Manger (ICM) which supports a novel bitstream relocation model to enable the placement of relocatable accelerators across several locations on the FPGA chip. In addition to supporting all the configuration capabilities required to implement a Reconfigurable Operating System (ROS), the proposed ICM also supports the novel multiple-clone configuration technique which allows for cloning several instances of the same hardware accelerator at the same time resulting in much shorter configuration time compared to traditional configuration techniques. A faulttolerant (FT) version of the proposed ICM which supports a comprehensive faultrecovery scheme is also introduced in this thesis. The proposed FT-ICM is designed with a much smaller area footprint compared to Triple Modular Redundancy (TMR) hardening techniques while keeping a comparable level of fault-resilience. The capabilities of the proposed ICM system are demonstrated with two novel applications. The first application demonstrates a proof-of-concept reliable FPGA server solution used for executing encryption/decryption queries. The proposed server deploys bitstream relocation and modular redundancy to mitigate both permanent and transient faults in the device. It also deploys a novel Built-In Self- Test (BIST) diagnosis scheme, specifically designed to detect emerging permanent faults in the system at run-time. The second application is a data mining application where DPR is used to increase the computational density of a system used to implement the Frequent Itemset Mining (FIM) problem