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  • 1.
    Navas, Byron
    KTH, School of Information and Communication Technology (ICT), Electronics and Embedded Systems.
    Cognitive and Self-Adaptive SoCs with Self-Healing Run-Time-Reconfigurable RecoBlocks2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In contrast to classical Field-Programmable Gate Arrays (FPGAs), partial and run-time reconfigurable (RTR) FPGAs can selectively reconfigure partitions of its hardware almost immediately while it is still powered and operative. In this way, RTR FPGAs combine the flexibility of software with the high efficiency of hardware. However, their potential cannot be fully exploited due to the increased complexity of the design process, and the intricacy to generate partial reconfigurations. FPGAs are often seen as a single auxiliary area to accelerate algorithms for specific problems. However, when several RTR partitions are implemented and combined with a processor system, new opportunities and challenges appear due to the creation of a heterogeneous RTR embedded system-on-chip (SoC).

    The aim of this thesis is to investigate how the flexibility, reusability, and productivity in the design process of partial and RTR embedded SoCs can be improved to enable research and development of novel applications in areas such as hardware acceleration, dynamic fault-tolerance, self-healing, self-awareness, and self-adaptation. To address this question, this thesis proposes a solution based on modular reconfigurable IP-cores and design-and-reuse principles to reduce the design complexity and maximize the productivity of such FPGA-based SoCs. The research presented in this thesis found inspiration in several related topics and sciences such as reconfigurable computing, dependability and fault-tolerance, complex adaptive systems, bio-inspired hardware, organic and autonomic computing, psychology, and machine learning.

    The outcome of this thesis demonstrates that the proposed solution addressed the research question and enabled investigation in initially unexpected fields. The particular contributions of this thesis are: (1) the RecoBlock SoC concept and platform with its flexible and reusable array of RTR IP-cores, (2) a simplified method to transform complex algorithms modeled in Matlab into relocatable partial reconfigurations adapted to an improved RecoBlock IP-core architecture, (3) the self-healing RTR fault-tolerant (FT) schemes, especially the Upset-Fault-Observer (UFO) that reuse available RTR IP-cores to self-assemble hardware redundancy during runtime, (4) the concept of Cognitive Reconfigurable Hardware (CRH) that defines a development path to achieve self-adaptation and cognitive development, (5) an adaptive self-aware and fault-tolerant RTR SoC that learns to adapt the RTR FT schemes to performance goals under uncertainty using rule-based decision making, (6) a method based on online and model-free reinforcement learning that uses a Q-algorithm to self-optimize the activation of dynamic FT schemes in performance-aware RecoBlock SoCs.

    The vision of this thesis proposes a new class of self-adaptive and cognitive hardware systems consisting of arrays of modular RTR IP-cores. Such a system becomes self-aware of its internal performance and learns to self-optimize the decisions that trigger the adequate self-organization of these RTR cores, i.e., to create dynamic hardware redundancy and self-healing, particularly while working in uncertain environments.

  • 2.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Camera and LCM IP-Cores for NIOS SOPC System2009In: 6th FPGAworld Conference, Academic Proceedings 2009, New York: ACM , 2009, p. 18-23Conference paper (Refereed)
    Abstract [en]

    This paper presents the development of IP-Cores to integrate the Terasic DC2 Camera and LCM (LCD Module) daughter boards into an Altera Nios System, so that the image can be further processed by embedded software or custom hardware instructions. Among other challenges overcome during this work are clock-domain crossing, synchronizing FIFO design, variable and pipelined burst control, multi-masters contention for system memory and image frame buffer switching. In addition, we designed software device drivers, and API functions intended for graphics, image processing and video control; which are part of the IP deliverables. In a brief, this work describes some concepts and methodologies involved in the creation of IP-Cores for an Altera SOPC; it also presents the results of the designed CAM-IP and LCM-IP Cores working in an application demo, which constitutes a real solution and a reference design.

  • 3.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Reinforcement Learning Based Self-Optimization of Dynamic Fault-Tolerant Schemes in Performance-Aware RecoBlock SoCs2015Report (Other academic)
    Abstract [en]

    Partial and run-time reconfiguration (RTR) technology has increased the range of opportunities and applications in the design of systems-on-chip (SoCs) based on Field-Programmable Gate Arrays (FPGAs). Nevertheless, RTR adds another complexity to the design process, particularly when embedded FPGAs have to deal with power and performance constraints uncertain environments. Embedded systems will need to make autonomous decisions, develop cognitive properties such as self-awareness and finally become self-adaptive to be deployed in the real world. Classico-line modeling and programming methods are inadequate to cope with unpredictable environments. Reinforcement learning (RL) methods have been successfully explored to solve these complex optimization problems mainly in workstation computers, yet they are rarely implemented in embedded systems. Disruptive integration technologies reaching atomic-scales will increase the probability of fabrication errors and the sensitivity to electromagnetic radiation that can generate single-event upsets (SEUs) in the configuration memory of FPGAs. Dynamic FT schemes are promising RTR hardware redundancy structures that improve dependability, but on the other hand, they increase memory system traffic. This article presents an FPGA-based SoC that is self-aware of its monitored hardware and utilizes an online RL method to self-optimize the decisions that maintain the desired system performance, particularly when triggering hardware acceleration and dynamic FT schemes on RTR IP-cores. Moreover, this article describes the main features of the RecoBlock SoC concept, overviews the RL theory, shows the Q-learning algorithm adapted for the dynamic fault-tolerance optimization problem, and presents its simulation in Matlab. Based on this investigation, the Q-learning algorithm will be implemented and verified in the RecoBlock SoC platform.

  • 4.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    The RecoBlock SoC Platform: A Flexible Array of Reusable Run-Time-Reconfigurable IP-Blocks2013In: Design, Automation & Test in Europe Conference & Exhibition (DATE), 2013, 2013, p. 833-838Conference paper (Refereed)
    Abstract [en]

    Run-time reconfigurable (RTR) FPGAs combine the flexibility of software with the high efficiency of hardware. Still, their potential cannot be fully exploited due to increased complexity of the design process. Consequently, to enable an efficient design flow, we devise a set of prerequisites to increase the flexibility and reusability of current FPGA-based RTR architectures. We apply these principles to design and implement the RecoBlock SoC platform, which main characterization is (1) a RTR plug-and-play IP-Core whose functionality is configured at run-time; (2) flexible inter-block communication configured via software, and (3) built-in buffers to support data-driven streams and inter-process communications. We illustrate the potential of our platform by a tutorial case study using an adaptive streaming application to investigate different combinations of reconfigurable arrays and schedules. The experiments underline the benefits of the platform and shows resource utilization.

  • 5.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Towards cognitive reconfigurable hardware: Self-aware learning in RTR fault-tolerant SoCs2015In: Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, article id 7238103Conference paper (Refereed)
    Abstract [en]

    Traditional embedded systems are evolving into power-and-performance-domain self-aware intelligent systems in order to overcome complexity and uncertainty. Without human control, they need to keep operative states in applications such as drone-based delivery or robotic space landing. Nowadays, the partial and run-time reconfiguration (RTR) of FPGA-based Systems-on-chip (SoC) can enable dynamic hardware acceleration or self-healing structures, but this conversely increases system-memory traffic. This paper introduces the basis of cognitive reconfigurable hardware and presents the design of an FPGA-based RTR SoC that becomes conscious of its monitored hardware and learns to make decisions that maintain a desired system performance, particularly when triggering hardware acceleration and dynamic fault-tolerant (FT) schemes on RTR cores. Self-awareness is achieved by evaluating monitored metrics in critical AXI-cores, supported by hardware performance counters. We suggest a reinforcement-learning algorithm that helps the system to search out when and which reconfigurable FT-scheme can be triggered. Executing random sequences of an embedded benchmark suite simulates unpredictability and bus traffic. The evaluation shows the effectiveness and implications of our approach.

  • 6.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems. ESPE Universidad de Las Fuerzas Armadas, Ecuador .
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    On providing scalable self-healing adaptive fault-tolerance to RTR SoCs2014In: Proceedings of ReConFigurable Computing and FPGAs (ReConFig), 2014 International Conference on, 2014, p. 1-6Conference paper (Refereed)
    Abstract [en]

    The dependability of heterogeneous many-core FPGA based systems are threatened by higher failure rates caused by disruptive scales of integration, increased design complexity, and radiation sensitivity. Triple-modular redundancy (TMR) and run-time reconfiguration (RTR) are traditional fault-tolerant (FT) techniques used to increase dependability. However, hardware redundancy is expensive and most approaches have poor scalability, flexibility, and programmability. Therefore, innovative solutions are needed to reduce the redundancy cost but still preserve acceptable levels of dependability. In this context, this paper presents the implementation of a self-healing adaptive fault-tolerant SoC that reuses RTR IP-cores in order to self-assemble different TMR schemes during run-time. The presented system demonstrates the feasibility of the Upset-Fault-Observer concept, which provides a run-time self-test and recovery strategy that delivers fault-tolerance over functions accelerated in RTR cores, at the same time reducing the redundancy scalability cost by running periodic reconfigurable TMR scan-cycles. In addition, this paper experimentally evaluates the trade-off of the implemented reconfigurable TMR schemes by characterizing important fault tolerant metrics i.e., recovery time (self-repair and self-replicate), detection latency, self-assembly latency, throughput reduction, and increase of physical resources.

  • 7.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    The Upset-Fault-Observer: A Concept for Self-healing Adaptive Fault Tolerance2014In: Proceedings of the 2014 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2014, IEEE Computer Society, 2014, p. 89-96Conference paper (Refereed)
    Abstract [en]

    Advancing integration reaching atomic-scales makes components highly defective and unstable during lifetime. This demands paradigm shifts in electronic systems design. FPGAs are particularly sensitive to cosmic and other kinds of radiations that produce single-event-upsets (SEU) in configuration and internal memories. Typical fault-tolerance (FT) techniques combine triple-modular-redundancy (TMR) schemes with run-time-reconfiguration (RTR). However, even the most successful approaches disregard the low suitability of fine-grain redundancy in nano-scale design, poor scalability and programmability of application specific architectures, small performance-consumption ratio of board-level designs, or scarce optimization capability of rigid redundancy structures. In that context, we introduce an innovative solution that exploits the flexibility, reusability, and scalability of a modular RTR SoC approach and reuse existing RTR IP-cores in order to assemble different TMR schemes during run-time. Thus, the system can adaptively trigger the adequate self-healing strategy according to execution environment metrics and user-defined goals. Specifically the paper presents: (a) the upset-fault-observer (UFO), an innovative run-time self-test and recovery strategy that delivers FT on request over several function cores but saves the redundancy scalability cost by running periodic reconfigurable TMR scan-cycles, (b) run-time reconfigurable TMR schemes and self-repair mechanisms, and (c) an adaptive software organization model to manage the proposed FT strategies.

  • 8.
    Navas, Byron
    et al.
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Öberg, Johnny
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Sander, Ingo
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    Towards the generic reconfigurable accelerator: Algorithm development, core design, and performance analysis2013Conference paper (Refereed)
    Abstract [en]

    Adoption of reconfigurable computing is limited in part by the lack of simplified, economic, and reusable solutions. The significant speedup and energy saving can increase performance but also design complexity; in particular for heterogeneous SoCs blending several CPUs, GPUs, and FPGA-Accelerator Cores. On the other hand, implementing complex algorithms in hardware requires modeling and verification, not only HDL generation. Most approaches are too specific without looking for reusability. Therefore, we present a solution based on: (1) a design methodology to develop algorithms accelerated in reconfigurable/non-reconfigurable IP-Cores, using common access tools, and contemplating verification from model to embedded software stages; (2) a generic accelerator core design that enables relocation and reuse almost independently of the algorithm, and data-flow driven execution models; and (3) a performance analysis of the acceleration mechanisms included in our system (i.e., accelerator core, burst I/O transfers, and reconfiguration pre-fetch). In consequence, the implemented system accelerates algorithms (e.g., FIR and Kalman filters) with speedups up to 3 orders of magnitude, compared to processor implementations.

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