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

Fault-tolerant satellite computing with modern semiconductors

Miniaturized satellites enable a variety space missions which were in the past infeasible, impractical or uneconomical with traditionally-designed heavier spacecraft. Especially CubeSats can be launched and manufactured rapidly at low cost from commercial components, even in academic environments. However, due to their low reliability and brief lifetime, they are usually not considered suitable for life- and safety-critical services, complex multi-phased solar-system-exploration missions, and missions with a longer duration. Commercial electronics are key to satellite miniaturization, but also responsible for their low reliability: Until 2019, there existed no reliable or fault-tolerant computer architectures suitable for very small satellites. To overcome this deficit, a novel on-board-computer architecture is described in this thesis.Robustness is assured without resorting to radiation hardening, but through software measures implemented within a robust-by-design multiprocessor-system-on-chip. This fault-tolerant architecture is component-wise simple and can dynamically adapt to changing performance requirements throughout a mission. It can support graceful aging by exploiting FPGA-reconfiguration and mixed-criticality.  Experimentally, we achieve 1.94W power consumption at 300Mhz with a Xilinx Kintex Ultrascale+ proof-of-concept, which is well within the powerbudget range of current 2U CubeSats. To our knowledge, this is the first COTS-based, reproducible on-board-computer architecture that can offer strong fault coverage even for small CubeSats.European Space AgencyComputer Systems, Imagery and Medi

Exploiting Hardware Abstraction for Parallel Programming Framework: Platform and Multitasking

With the help of the parallelism provided by the fine-grained architecture, hardware accelerators on Field Programmable Gate Arrays (FPGAs) can significantly improve the performance of many applications. However, designers are required to have excellent hardware programming skills and unique optimization techniques to explore the potential of FPGA resources fully. Intermediate frameworks above hardware circuits are proposed to improve either performance or productivity by leveraging parallel programming models beyond the multi-core era. In this work, we propose the PolyPC (Polymorphic Parallel Computing) framework, which targets enhancing productivity without losing performance. It helps designers develop parallelized applications and implement them on FPGAs. The PolyPC framework implements a custom hardware platform, on which programs written in an OpenCL-like programming model can launch. Additionally, the PolyPC framework extends vendor-provided tools to provide a complete development environment including intermediate software framework, and automatic system builders. Designers\u27 programs can be either synthesized as hardware processing elements (PEs) or compiled to executable files running on software PEs. Benefiting from nontrivial features of re-loadable PEs, and independent group-level schedulers, the multitasking is enabled for both software and hardware PEs to improve the efficiency of utilizing hardware resources. The PolyPC framework is evaluated regarding performance, area efficiency, and multitasking. The results show a maximum 66 times speedup over a dual-core ARM processor and 1043 times speedup over a high-performance MicroBlaze with 125 times of area efficiency. It delivers a significant improvement in response time to high-priority tasks with the priority-aware scheduling. Overheads of multitasking are evaluated to analyze trade-offs. With the help of the design flow, the OpenCL application programs are converted into executables through the front-end source-to-source transformation and back-end synthesis/compilation to run on PEs, and the framework is generated from users\u27 specifications

Adaptive Intelligent Systems for Extreme Environments

As embedded processors become powerful, a growing number of embedded systems equipped with artificial intelligence (AI) algorithms have been used in radiation environments to perform routine tasks to reduce radiation risk for human workers. On the one hand, because of the low price, commercial-off-the-shelf devices and components are becoming increasingly popular to make such tasks more affordable. Meanwhile, it also presents new challenges to improve radiation tolerance, the capability to conduct multiple AI tasks and deliver the power efficiency of the embedded systems in harsh environments. There are three aspects of research work that have been completed in this thesis: 1) a fast simulation method for analysis of single event effect (SEE) in integrated circuits, 2) a self-refresh scheme to detect and correct bit-flips in random access memory (RAM), and 3) a hardware AI system with dynamic hardware accelerators and AI models for increasing flexibility and efficiency. The variances of the physical parameters in practical implementation, such as the nature of the particle, linear energy transfer and circuit characteristics, may have a large impact on the final simulation accuracy, which will significantly increase the complexity and cost in the workflow of the transistor level simulation for large-scale circuits. It makes it difficult to conduct SEE simulations for large-scale circuits. Therefore, in the first research work, a new SEE simulation scheme is proposed, to offer a fast and cost-efficient method to evaluate and compare the performance of large-scale circuits which subject to the effects of radiation particles. The advantages of transistor and hardware description language (HDL) simulations are combined here to produce accurate SEE digital error models for rapid error analysis in large-scale circuits. Under the proposed scheme, time-consuming back-end steps are skipped. The SEE analysis for large-scale circuits can be completed in just few hours. In high-radiation environments, bit-flips in RAMs can not only occur but may also be accumulated. However, the typical error mitigation methods can not handle high error rates with low hardware costs. In the second work, an adaptive scheme combined with correcting codes and refreshing techniques is proposed, to correct errors and mitigate error accumulation in extreme radiation environments. This scheme is proposed to continuously refresh the data in RAMs so that errors can not be accumulated. Furthermore, because the proposed design can share the same ports with the user module without changing the timing sequence, it thus can be easily applied to the system where the hardware modules are designed with fixed reading and writing latency. It is a challenge to implement intelligent systems with constrained hardware resources. In the third work, an adaptive hardware resource management system for multiple AI tasks in harsh environments was designed. Inspired by the “refreshing” concept in the second work, we utilise a key feature of FPGAs, partial reconfiguration, to improve the reliability and efficiency of the AI system. More importantly, this feature provides the capability to manage the hardware resources for deep learning acceleration. In the proposed design, the on-chip hardware resources are dynamically managed to improve the flexibility, performance and power efficiency of deep learning inference systems. The deep learning units provided by Xilinx are used to perform multiple AI tasks simultaneously, and the experiments show significant improvements in power efficiency for a wide range of scenarios with different workloads. To further improve the performance of the system, the concept of reconfiguration was further extended. As a result, an adaptive DL software framework was designed. This framework can provide a significant level of adaptability support for various deep learning algorithms on an FPGA-based edge computing platform. To meet the specific accuracy and latency requirements derived from the running applications and operating environments, the platform may dynamically update hardware and software (e.g., processing pipelines) to achieve better cost, power, and processing efficiency compared to the static system

Dynamisch partielle Rekonfiguration in fehlertoleranten FPGA-Systemen

Korf S. Dynamisch partielle Rekonfiguration in fehlertoleranten FPGA-Systemen. Bielefeld: Universität Bielefeld; 2017.Die Anforderungen an mikroelektronische Systeme steigen kontinuierlich. Rekonfigurierbare Architekturen bieten einen Kompromiss zwischen der Leistungsfähigkeit anwendungsspezifischer Schaltungen (ASICs) und der Flexibilität heutiger Prozessoren. Sogenannte im Feld programmierbare Gatter-Arrays (engl. Field-Programmable Gate Arrays, FPGAs) haben sich hierbei in den letzten Jahrzehnten besonders etabliert. Die Konfigurationsart dynamisch partielle Rekonfiguration (DPR) moderner SRAM-basierter FPGAs verdeutlicht die gewonnene System-Flexibilität. DPR wird in verschiedensten Anwendungsgebieten aus unterschiedlichsten Motivationen heraus eingesetzt. Die Hauptanwendung der DPR ist die Erstellung eines Systems, welches Veränderungen an der Schaltung auf dem FPGA zur Laufzeit erlaubt. Obwohl viele FPGA-Familien bereits seit zwei Jahrzehnten DPR hardwareseitig ermöglichen, ist die Unterstützung durch die Hersteller-Software und insbesondere die Eigenschaften des daraus resultierenden DPR-Systems verbesserungswürdig. Um das Potenzial der verfügbaren Hardware-Flexibilität ausnutzen zu können, wird in dieser Dissertation ein neuer Entwurfsablauf (INDRA 2.0, INtegrated Design Flow for Reconfigurable Architectures) vorgestellt. Dieser Entwurfsablauf ermöglicht die Erstellung eines flexiblen DPR-Systems mit geringem Speicher-, Verwaltungs- und Wartungsaufwand. Für Anwendungen mit Homogenitätsanforderungen wird mit DHHarMa (Design Flow for Homogeneous Hard Macros) ein Entwurfsablauf vorgestellt, der die Transformation eines zunächst inhomogenen Designs in ein homogenes Design ermöglicht. Bei dieser Design-Homogenisierung ergibt sich die Fragestellung nach möglichen Auswirkungen bezüglich des FPGA-Ressourcenbedarfs und der Leistungsfähigkeit durch die einzelnen Homogenisierungsschritte. Die einzelnen DHHarMa-Softwarekomponenten wurden daher detailliert durch verschiedene Bewertungsmaße analysiert. Hierbei konnte festgehalten werden, dass die Homogenisierungsschritte im Mittel einen, teils deutlichen, positiven Einfluss auf den FPGA-Ressourcenbedarf jedoch teils einen geringen negativen Einfluss auf die Leistungsfähigkeit hat. Die verwendete FPGA-Architektur hat hierbei auf beide Größen einen entscheidenden Einfluss. Zusätzlich wird in Anwendungsgebieten mit Strahlungseinfluss die DPR-Funktionalität in Verfahren zur Abschwächung von durch Strahlung induzierten Fehlern eingesetzt. In der Dissertation wird mit der Readback Scrubbing Unit eine Komponente vorgestellt, welche eine Einbitfehlerkorrektur und Zweibitfehlererkennung im FPGA-Konfigurationsspeicher implementiert. Durch integrierte Fehlerstatistikmechanismen wird eine Analyse des Systems zur Laufzeit realisiert. Zusätzlich ist die Erstellung von Readback Scrubbing Schedules möglich, sodass die Fehlererkennung und -korrektur zum einen autonom und zum anderen zur Laufzeit angepasst werden kann. Zusätzlich wird mit OLT(RE)² (On-Line on-demand Testing approach for permanent Radiation Effects in REconfigurable systems) ein Selbsttest für ein SRAM-basiertes FPGA vorgestellt. Dieser Selbsttest ermöglicht zur Systemlaufzeit eine Überprüfung einer FPGA-Fläche vor der Verwendung auf permanente Fehler in den Verdrahtungsressourcen