Les systèmes embarqués mobiles font partis intégrante de notre quotidien. Afin de les rendre plus adaptésaux usages, ils ont été miniaturisés et leur autonomie a été augmentée, parfois de façon très considérable.Toutefois, les propositions d’amélioration butent désormais sur les possibilités de la technologie des circuitsintégrés. Pour aller plus loin, il faut donc envisager de repenser la chaîne de traitement du signal afin deréduire la consommation de ces dispositifs. Cette thèse développe une approche originale pour exploiterefficacement l’échantillonnage par traversée de niveaux d’une part et, d’autre part, associe cet échantillonnageà une logique évènementielle afin de réduire drastiquement la consommation d’énergie des systèmesintégrés autonomes. Une méthode de discrétisation adaptée à une application de reconnaissance de signauxphysiologiques, utilisée comme exemple dans cette thèse, y est présentée. Un premier prototype en logiqueévènementielle (asynchrone) sur circuit FPGA a permis de valider cette stratégie et de démontrer les bénéficesde cet échantillonnage dédié en termes de réduction de l’activité par rapport à un échantillonnage uniforme.Un second prototype en logique asynchrone et conçu en technologie CMOS AMS 0.35 μm a permis de validerpar simulation électrique un gain extrêmement important sur la consommation électrique du dispositif. / Our everyday life is highly dependent on mobile embedded systems. In order to make them suitable to differentapplications, they have underwent size reduction and lifetime extension. However, these improvementsare currently limited by the possibilities of the integrated circuits technologies. In order to push back theboundaries, it is necessary to reconsider the whole digital signal processing chain from scratch to sustain thepower consumption reduction in this kind of system. This work develops on the first hand a strategy thatsmartly uses the level-crossing sampling scheme and on the other combines this sampling method with eventlogicto highly reduce the power consumption in mobile embedded systems. A discretisation method adaptedto the recognition of physiological patterns application is described. A first event-logic (asynchronous) prototypeimplemented on FPGA proved the potential benefits that an adapted sampling scheme could offersto reduce activity compared to a uniform sampling scheme. Electrical simulations performed on a secondprototype, also designed in asynchronous logic, with CMOS AMS 0.35 μm technology, validated a high gainin power consumption.

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche.

This master thesis is focused on the theoretical description and practical implementation of software defined transmitter. The main aim of this thesis was made the prototype of software defined transmitter in FM band. Theoretical part is determined to description of basic parts of equipment and working principles to understand the basic principle of digital transmitters and define the appropriate component base for construction. Discussed are used types of A/D and D/A converters, blocks of digital signal processing and the roles, which these components performs. The second part is focused practical. Specified are suitable types of components and block diagram is proposed for following electrical connection and printed circuit board in Eagle program as a plug-in modul for developmental platform Arduino. The main point is program, which sets and controls the transmitter. Next important part is impedance match and antenna tuning, which is explain in practical part of thesis. The result is prototype of software defined transmitter compatible with Arduino Uno platform.

Ziel der Arbeit ist die anschauliche Demonstration der Leistungsfähigkeit von Hardware- Systemen zur Regelung instabiler Systeme am Beispiel des Inversen Pendels. Dabei handelt es sich um das Balancieren eines Stabes, einem Standard-Problem der Regelungstechnik. Es wird die Konzeption und Implementierung einer Hardware-Regelung in einem FPGA-Prototypenboard zur Realisierung dieser Aufgabe beschrieben. Die Regelung basiert mit LQR-Entwurf und Kalman-Filter auf klassischen Methoden der Regelungstechnik. Zur Demonstration der Regelung wurde ein mechanischer Aufbau vorgenommen, an dem die Funktionsfähigkeit des Inversen Pendels praktisch gezeigt wurde.:1 Einleitung 11 1.1 Motivation.................................... 11 1.2 Analyse der Aufgabenstellung ......................... 11 1.3 Gliederung der Arbeit ............................. 12 2 Grundlagen 13 2.1 Referenzanwendung Inverses Pendel...................... 13 2.2 Aufbau des Regelsystems............................ 15 2.3 Verfahren zum Reglerentwurf ......................... 19 2.4 Verfahren zur Zustandsschätzung ....................... 22 2.5 Sensorik..................................... 26 2.6 Antrieb ..................................... 28 3 Inverse Pendel im Vergleich 33 3.1 Bauformen.................................... 33 3.2 Realisierungsbeispiele.............................. 34 3.3 Fazit der Recherche............................... 39 4 Elektromechanischer Aufbau 41 4.1 Mechanik .................................... 42 4.2 Sensorik..................................... 42 4.3 Antrieb ..................................... 44 4.4 FPGA-Board .................................. 47 5 Modellbildung 48 5.1 Herleitung der Systemgleichungen....................... 48 5.2 Anpassung an den Schrittmotor ........................ 51 5.3 Linearisiertes Modell im Zustandsraum.................... 51 5.4 Analyse der Modelleigenschaften........................ 52 6 Reglerentwurf 56 6.1 Einstellung des LQ-Reglers........................... 56 6.2 Einstellung des Kalman-Filters ........................ 57 6.3 Aufschwing- und Fangalgorithmus....................... 58 6.4 Simulation.................................... 60 6.5 Konsequenzen für die Realisierung....................... 63 7 Implementierung 65 7.1 Besonderheiten des Hardware-Entwurfs.................... 65 7.2 Systempartitionierung und Entwurfsstrategie . . . . . . . . . . . . . . . . . 66 7.3 Teilkomponenten ................................ 67 7.4 Modifizierung des Kalman-Filters ....................... 72 7.5 Probleme .................................... 78 8 Schluss 80 8.1 Zusammenfassung................................ 80 8.2 Ergebnisse.................................... 80 8.3 Ausblick..................................... 82 Literaturverzeichnis 84 A Details zum Projekt 87 A.1 Kurzdokumentation .............................. 87 A.2 Datei- und Verzeichnisstruktur......................... 88 A.3 Simulationsresultate .............................. 89

The goal of the QPACE project is to build a novel cost-efficient massive parallel supercomputer optimized for LQCD (Lattice Quantum Chromodynamics) applications. Unlike previous projects which use custom ASICs, this is accomplished by using the general purpose multi-core CPU PowerXCell 8i processor tightly coupled with a custom network processor implemented on a modern FPGA. The heterogeneous architecture of the PowerXCell 8i processor and its core-independent OS-bypassing access to the custom network hardware and application-oriented 3D torus topology pose interesting challenges for the implementation of the applications. This work will describe and evaluate the implementation possibilities of message passing APIs: the more general MPI, and the more QCD-oriented QMP, and their performance in PPE centric or SPE centric scenarios. These results will then be employed to optimize HPL for the QPACE architecture. Finally, the developed approaches and concepts will be briefly discussed regarding their applicability to heterogeneous node/network architectures as is the case in the "High-speed Network Interface with Collective Operation Support for Cell BE (NICOLL)" project.

This thesis investigates the use of field-programmable gate arrays (FPGAs) to implement a time-to-digital converter (TDC) with on-chip calibration and temperature correction.Using carry-chains on the Xilinx Kintex UltraScale architecture to create a tapped delay line (TDL) has previously been proven to give good time resolution.This project improves the resolution further by using a bit-counter to handle bubbles in the TDL without removing any taps.The bit counter also adds the possibility of using a wave-union approach previously dismissed as unusable on this architecture.The final implementation achieves an RMS resolution of 1.8 ps.

Partial dynamic reconfiguration of FPGAs has attracted high attention from both academia and industry in recent years. With this technique, the functionality of the programmable devices can be adapted at runtime to changing requirements. The approach allows designers to use FPGAs more efficiently: E. g. FPGA resources can be time-shared between different functions and the functions itself can be adapted to changing workloads at runtime. Thus partial dynamic reconfiguration enables a unique combination of software-like flexibility and hardware-like performance. Still there exists no common understanding on how to assess the overhead introduced by partial dynamic reconfiguration. This dissertation presents a new cost model for both the runtime and the memory overhead that results from partial dynamic reconfiguration. It is shown how the model can be incorporated into all stages of the design optimization for reconfigurable hardware. In particular digital circuits can be mapped onto FPGAs such that only small fractions of the hardware must be reconfigured at runtime, which saves time, memory, and energy. The design optimization is most efficient if it is applied during high level synthesis. This book describes how the cost model has been integrated into a new high level synthesis tool. The tool allows the designer to trade-off FPGA resource use versus reconfiguration overhead. It is shown that partial reconfiguration causes only small overhead if the design is optimized with regard to reconfiguration cost. A wide range of experimental results is provided that demonstrates the benefits of the applied method.:1 Introduction 1 1.1 Reconfigurable Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Reconfigurable System on a Chip (RSOC) . . . . . . . . . . . . 4 1.1.2 Anatomy of an Application . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 RSOC Design Characteristics and Trade-offs . . . . . . . . . . . 7 1.2 Classification of Reconfigurable Architectures . . . . . . . . . . . . . . . 10 1.2.1 Partial Reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.2 Runtime Reconfiguration (RTR) . . . . . . . . . . . . . . . . . . . 10 1.2.3 Multi-Context Configuration . . . . . . . . . . . . . . . . . . . . . 11 1.2.4 Fine-Grain Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Coarse-Grain Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Reconfigurable Computing Specific Design Issues . . . . . . . . . . . . 12 1.4 Overview of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Reconfigurable Computing Systems – Background 17 2.1 Examples for RSOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Partially Reconfigurable FPGAs: Xilinx Virtex Device Family . . . . . . 20 2.2.1 Virtex-II/Virtex-II Pro Logic Architecture . . . . . . . . . . . . . 20 2.2.2 Reconfiguration Architecture and Reconfiguration Control . . 21 2.3 Methods for Design Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.1 Behavioural Design Entry . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2 Design Entry at Register-Transfer Level (RTL) . . . . . . . . . . 25 2.3.3 Xilinx Early Access Partial Reconfiguration Design Flow . . . . 26 2.4 Task Management in Reconfigurable Computing . . . . . . . . . . . . . 27 2.4.1 Online and Offline Task Management . . . . . . . . . . . . . . . 28 2.4.2 Task Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Task Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.4 Reconfiguration Runtime Overhead . . . . . . . . . . . . . . . . 31 2.5 Configuration Data Compression . . . . . . . . . . . . . . . . . . . . . . . 32 2.6 Evaluation of Reconfigurable Systems . . . . . . . . . . . . . . . . . . . . 35 2.6.1 Energy Efficiency Models . . . . . . . . . . . . . . . . . . . . . . . 35 2.6.2 Area Efficiency Models . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6.3 Runtime Efficiency Models . . . . . . . . . . . . . . . . . . . . . . 37 2.7 Similarity Based Reduction of Reconfiguration Overhead . . . . . . . . 38 2.7.1 Configuration Data Generation Methods . . . . . . . . . . . . . 39 2.7.2 Device Mapping Methods . . . . . . . . . . . . . . . . . . . . . . . 40 2.7.3 Circuit Design Methods . . . . . . . . . . . . . . . . . . . . . . . . 41 2.7.4 Model for Partial Configuration . . . . . . . . . . . . . . . . . . . 44 2.8 Contributions of this Work . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 Runtime Reconfiguration Cost and Optimization Methods 47 3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.2 Reconfiguration State Graph . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.1 Reconfiguration Time Overhead . . . . . . . . . . . . . . . . . . 52 3.2.2 Dynamic Configuration Data Overhead . . . . . . . . . . . . . . 52 3.3 Configuration Cost at Bitstream Level . . . . . . . . . . . . . . . . . . . . 54 3.4 Configuration Cost at Structural Level . . . . . . . . . . . . . . . . . . . 56 3.4.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.4.2 Virtual Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.4.3 Reconfiguration Costs in the VA Context . . . . . . . . . . . . . 65 3.5 Allocation Functions with Minimal Reconfiguration Costs . . . . . . . 67 3.5.1 Allocation of Node Pairs . . . . . . . . . . . . . . . . . . . . . . . 68 3.5.2 Direct Allocation of Nodes . . . . . . . . . . . . . . . . . . . . . . 76 3.5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4 Implementation Tools for Reconfigurable Computing 95 4.1 Mapping of Netlists to FPGA Resources . . . . . . . . . . . . . . . . . . . 96 4.1.1 Mapping to Device Resources . . . . . . . . . . . . . . . . . . . . 96 4.1.2 Connectivity Transformations . . . . . . . . . . . . . . . . . . . . 99 4.1.3 Mapping Variants and Reconfiguration Costs . . . . . . . . . . . 100 4.1.4 Mapping of Circuit Macros . . . . . . . . . . . . . . . . . . . . . . 101 4.1.5 Global Interconnect . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.1.6 Netlist Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2 Mapping Aware Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2.1 Generalized Node Mapping . . . . . . . . . . . . . . . . . . . . . 104 4.2.2 Successive Node Allocation . . . . . . . . . . . . . . . . . . . . . 105 4.2.3 Node Allocation with Ant Colony Optimization . . . . . . . . . 107 4.2.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.3 Netlist Mapping with Minimized Reconfiguration Cost . . . . . . . . . 110 4.3.1 Mapping Database . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.3.2 Mapping and Packing of Elements into Logic Blocks . . . . . . 112 4.3.3 Logic Element Selection . . . . . . . . . . . . . . . . . . . . . . . 114 4.3.4 Logic Element Selection for Min. Routing Reconfiguration . . 115 4.3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5 High-Level Synthesis for Reconfigurable Computing 125 5.1 Introduction to HLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1.1 HLS Tool Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1.2 Realization of the Hardware Tasks . . . . . . . . . . . . . . . . . 128 5.2 New Concepts for Task-based Reconfiguration . . . . . . . . . . . . . . 131 5.2.1 Multiple Hardware Tasks in one Reconfigurable Module . . . . 132 5.2.2 Multi-Level Reconfiguration . . . . . . . . . . . . . . . . . . . . . 133 5.2.3 Resource Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.3 Datapath Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.3.1 Task Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.3.2 Resource Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.3.3 Resource Binding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.3.4 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.5 Constraints for Scheduling and Resource Binding . . . . . . . . 151 5.4 Reconfiguration Optimized Datapath Implementation . . . . . . . . . . 153 5.4.1 Effects of Scheduling and Binding on Reconfiguration Costs . 153 5.4.2 Strategies for Resource Type Binding . . . . . . . . . . . . . . . 154 5.4.3 Strategies for Resource Instance Binding . . . . . . . . . . . . . 157 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.5.1 Summary of Binding Methods and Tool Setup . . . . . . . . . . 163 5.5.2 Cost Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 5.5.3 Implementation Scenarios . . . . . . . . . . . . . . . . . . . . . . 166 5.5.4 Benchmark Characteristics . . . . . . . . . . . . . . . . . . . . . . 168 5.5.5 Benchmark Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 6 Summary and Outlook 185 Bibliography 189 A Simulated Annealing 201 / Partielle dynamische Rekonfiguration von FPGAs hat in den letzten Jahren große Aufmerksamkeit von Wissenschaft und Industrie auf sich gezogen. Die Technik erlaubt es, die Funktionalität von progammierbaren Bausteinen zur Laufzeit an veränderte Anforderungen anzupassen. Dynamische Rekonfiguration erlaubt es Entwicklern, FPGAs effizienter einzusetzen: z.B. können Ressourcen für verschiedene Funktionen wiederverwendet werden und die Funktionen selbst können zur Laufzeit an veränderte Verarbeitungsschritte angepasst werden. Insgesamt erlaubt partielle dynamische Rekonfiguration eine einzigartige Kombination von software-artiger Flexibilität und hardware-artiger Leistungsfähigkeit. Bis heute gibt es keine Übereinkunft darüber, wie der zusätzliche Aufwand, der durch partielle dynamische Rekonfiguration verursacht wird, zu bewerten ist. Diese Dissertation führt ein neues Kostenmodell für Laufzeit und Speicherbedarf ein, welche durch partielle dynamische Rekonfiguration verursacht wird. Es wird aufgezeigt, wie das Modell in alle Ebenen der Entwurfsoptimierung für rekonfigurierbare Hardware einbezogen werden kann. Insbesondere wird gezeigt, wie digitale Schaltungen derart auf FPGAs abgebildet werden können, sodass nur wenig Ressourcen der Hardware zur Laufzeit rekonfiguriert werden müssen. Dadurch kann Zeit, Speicher und Energie eingespart werden. Die Entwurfsoptimierung ist am effektivsten, wenn sie auf der Ebene der High-Level-Synthese angewendet wird. Diese Arbeit beschreibt, wie das Kostenmodell in ein neuartiges Werkzeug für die High-Level-Synthese integriert wurde. Das Werkzeug erlaubt es, beim Entwurf die Nutzung von FPGA-Ressourcen gegen den Rekonfigurationsaufwand abzuwägen. Es wird gezeigt, dass partielle Rekonfiguration nur wenig Kosten verursacht, wenn der Entwurf bezüglich Rekonfigurationskosten optimiert wird. Eine Anzahl von Beispielen und experimentellen Ergebnissen belegt die Vorteile der angewendeten Methodik.:1 Introduction 1 1.1 Reconfigurable Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Reconfigurable System on a Chip (RSOC) . . . . . . . . . . . . 4 1.1.2 Anatomy of an Application . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 RSOC Design Characteristics and Trade-offs . . . . . . . . . . . 7 1.2 Classification of Reconfigurable Architectures . . . . . . . . . . . . . . . 10 1.2.1 Partial Reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.2 Runtime Reconfiguration (RTR) . . . . . . . . . . . . . . . . . . . 10 1.2.3 Multi-Context Configuration . . . . . . . . . . . . . . . . . . . . . 11 1.2.4 Fine-Grain Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Coarse-Grain Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Reconfigurable Computing Specific Design Issues . . . . . . . . . . . . 12 1.4 Overview of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Reconfigurable Computing Systems – Background 17 2.1 Examples for RSOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Partially Reconfigurable FPGAs: Xilinx Virtex Device Family . . . . . . 20 2.2.1 Virtex-II/Virtex-II Pro Logic Architecture . . . . . . . . . . . . . 20 2.2.2 Reconfiguration Architecture and Reconfiguration Control . . 21 2.3 Methods for Design Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.1 Behavioural Design Entry . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2 Design Entry at Register-Transfer Level (RTL) . . . . . . . . . . 25 2.3.3 Xilinx Early Access Partial Reconfiguration Design Flow . . . . 26 2.4 Task Management in Reconfigurable Computing . . . . . . . . . . . . . 27 2.4.1 Online and Offline Task Management . . . . . . . . . . . . . . . 28 2.4.2 Task Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Task Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.4 Reconfiguration Runtime Overhead . . . . . . . . . . . . . . . . 31 2.5 Configuration Data Compression . . . . . . . . . . . . . . . . . . . . . . . 32 2.6 Evaluation of Reconfigurable Systems . . . . . . . . . . . . . . . . . . . . 35 2.6.1 Energy Efficiency Models . . . . . . . . . . . . . . . . . . . . . . . 35 2.6.2 Area Efficiency Models . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6.3 Runtime Efficiency Models . . . . . . . . . . . . . . . . . . . . . . 37 2.7 Similarity Based Reduction of Reconfiguration Overhead . . . . . . . . 38 2.7.1 Configuration Data Generation Methods . . . . . . . . . . . . . 39 2.7.2 Device Mapping Methods . . . . . . . . . . . . . . . . . . . . . . . 40 2.7.3 Circuit Design Methods . . . . . . . . . . . . . . . . . . . . . . . . 41 2.7.4 Model for Partial Configuration . . . . . . . . . . . . . . . . . . . 44 2.8 Contributions of this Work . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 Runtime Reconfiguration Cost and Optimization Methods 47 3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.2 Reconfiguration State Graph . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.1 Reconfiguration Time Overhead . . . . . . . . . . . . . . . . . . 52 3.2.2 Dynamic Configuration Data Overhead . . . . . . . . . . . . . . 52 3.3 Configuration Cost at Bitstream Level . . . . . . . . . . . . . . . . . . . . 54 3.4 Configuration Cost at Structural Level . . . . . . . . . . . . . . . . . . . 56 3.4.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.4.2 Virtual Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.4.3 Reconfiguration Costs in the VA Context . . . . . . . . . . . . . 65 3.5 Allocation Functions with Minimal Reconfiguration Costs . . . . . . . 67 3.5.1 Allocation of Node Pairs . . . . . . . . . . . . . . . . . . . . . . . 68 3.5.2 Direct Allocation of Nodes . . . . . . . . . . . . . . . . . . . . . . 76 3.5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4 Implementation Tools for Reconfigurable Computing 95 4.1 Mapping of Netlists to FPGA Resources . . . . . . . . . . . . . . . . . . . 96 4.1.1 Mapping to Device Resources . . . . . . . . . . . . . . . . . . . . 96 4.1.2 Connectivity Transformations . . . . . . . . . . . . . . . . . . . . 99 4.1.3 Mapping Variants and Reconfiguration Costs . . . . . . . . . . . 100 4.1.4 Mapping of Circuit Macros . . . . . . . . . . . . . . . . . . . . . . 101 4.1.5 Global Interconnect . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.1.6 Netlist Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2 Mapping Aware Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2.1 Generalized Node Mapping . . . . . . . . . . . . . . . . . . . . . 104 4.2.2 Successive Node Allocation . . . . . . . . . . . . . . . . . . . . . 105 4.2.3 Node Allocation with Ant Colony Optimization . . . . . . . . . 107 4.2.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.3 Netlist Mapping with Minimized Reconfiguration Cost . . . . . . . . . 110 4.3.1 Mapping Database . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.3.2 Mapping and Packing of Elements into Logic Blocks . . . . . . 112 4.3.3 Logic Element Selection . . . . . . . . . . . . . . . . . . . . . . . 114 4.3.4 Logic Element Selection for Min. Routing Reconfiguration . . 115 4.3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5 High-Level Synthesis for Reconfigurable Computing 125 5.1 Introduction to HLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1.1 HLS Tool Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1.2 Realization of the Hardware Tasks . . . . . . . . . . . . . . . . . 128 5.2 New Concepts for Task-based Reconfiguration . . . . . . . . . . . . . . 131 5.2.1 Multiple Hardware Tasks in one Reconfigurable Module . . . . 132 5.2.2 Multi-Level Reconfiguration . . . . . . . . . . . . . . . . . . . . . 133 5.2.3 Resource Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.3 Datapath Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.3.1 Task Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.3.2 Resource Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.3.3 Resource Binding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.3.4 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.5 Constraints for Scheduling and Resource Binding . . . . . . . . 151 5.4 Reconfiguration Optimized Datapath Implementation . . . . . . . . . . 153 5.4.1 Effects of Scheduling and Binding on Reconfiguration Costs . 153 5.4.2 Strategies for Resource Type Binding . . . . . . . . . . . . . . . 154 5.4.3 Strategies for Resource Instance Binding . . . . . . . . . . . . . 157 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.5.1 Summary of Binding Methods and Tool Setup . . . . . . . . . . 163 5.5.2 Cost Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 5.5.3 Implementation Scenarios . . . . . . . . . . . . . . . . . . . . . . 166 5.5.4 Benchmark Characteristics . . . . . . . . . . . . . . . . . . . . . . 168 5.5.5 Benchmark Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 6 Summary and Outlook 185 Bibliography 189 A Simulated Annealing 201

Scannende MEMS-Mikrospiegel stellen eine vielversprechende technologische Entwicklung mit potentiellen Anwendungen im Bereich der miniaturisierten Bildprojektion und Umgebungssensierung dar. Im Regelfall oszilliert das Spiegelelement hierbei resonant um die horizontale Achse, während die vertikale Achse statisch ausgelenkt wird. Somit ergibt sich ein sogenannter Raster-Scan. Während eine resonante Aktuierung in MEMS-Technologie im Frequenzbereich mehrerer kHz effizient umgesetzt werden kann, stellt die Implementierung statischer Antriebe eine Herausforderung dar. In dieser Arbeit wird ein neuartiges Aktuierungskonzept vorgestellt, das effizientere quasi-statische Auslenkung ermöglicht. Hierfür wird der Drehimpuls, der durch die hochfrequente horizontale Schwingung erzeugt wird, durch eine weitere resonante Oszillation ähnlicher Frequenz gestört, wodurch sich ein für die quasi-statische Auslenkung nutzbares Drehmoment ergibt. Da gyroskopische Effekte ausgenutzt werden, die nicht in aktuellen auf Modalanalyse basierenden Simulationsmethoden berücksichtigt sind, werden Starrkörper- und transiente FEM-Modelle entwickelt, um die Realisierbarkeit des Antriebskonzepts simulatorisch zu verifizieren. Im Rahmen der durch den genutzten Prozess gegebenen Randbedingungen werden daraufhin Aktuierungselemente für die resonanten Achsen erarbeitet und mit diesen zwei Designvarianten eines 2D-Mikrospiegels erstellt. Nach modellbasierter Verifikation werden diese in einer MEMS-Fertigungslinie prozessiert. Mit den generierten Mustern wird dann eine vollständige experimentelle Charakterisierung unter Nutzung eines speziell erstellten FPGA-basierten Evaluations-Boards durchgeführt. Beide Design-Varianten zeigen hierbei voll funktionsfähige Sensierungs- und Aktuierungselemente. Es kann ein erfolgreicher Nachweis der Funktionsfähigkeit des neuartigen Antriebskonzepts vollbracht werden. Die dabei gezeigte 2D-Projektion erreicht Winkel von 12° x 1.8° / Scanning MEMS micromirrors are an emergent technology for compact form factor image projection and environment sensing applications. Commonly the mirror element oscillates resonantly along the horizontal axis, whereas it is deflected statically along the vertical axis, performing a so called raster scan. While resonant actuation can be implemented efficiently in MEMS, static deflection however remains challenging. In this thesis a novel actuation concept for 2D MEMS micromirrors is introduced that potentially enables efficient quasi-static actuation. Therefore the angular momentum that is generated by the high frequency resonant axis is disturbed by an orthogonal resonant oscillation of similar frequency, leading to a torque that can be utilized to achieve an indirect quasi-static deflection. As in this case gyroscopic effects are exploited that are usually not considered in state of the art modal finite element based MEMS simulation, in order to validate the feasibility of the actuation concept rigid body and transient finite element based models are developed and simulation studies conducted. Using an existing manufacturing process as a framework, actuation schemes for the resonant axes are introduced and two distinct micromirror designs are developed and verified by simulation. These are processed in a MEMS manufacturing line. A thorough characterization study is then carried out using a custom FPGA based evaluation board with closed loop control capabilities. Both design variants are functional with regard to all actuation and tilt angle detection elements. A successful implementation of the proposed actuation concept is shown achieving 2D projection of a laser beam with tilt angles of 12 ◦ × 1.8 ◦ in frequency and amplitude controlled operation.

<div>Electrical nerve stimulation (ENS) is an emerging therapy for many neurological disorders. Compared with conventional one-way stimulations, closed-loop ENS approaches increase the stimulation efficacy and minimize patient's discomfort by constantly adjusting the stimulation parameters according to the feedback biomarkers from patients. Wireless neurostimulation devices capable of both stimulation and telemetry of recorded physiological signals are welcome for closed-loop ENS systems to improve the quality and reduce the costs of treatments, and real-time digital signal processing (DSP) engines processing and extracting features from recorded signals can reduce the data transmission rate and the resulting power consumption of wireless devices. Electrically-evoked compound action potential (ECAP) is an objective measure of nerve activity and has been used as the feedback biomarker in closed-loop ENS systems including neural response telemetry (NRT) systems and a newly proposed autonomous nerve control (ANC) platform. It's desirable to design a DSP engine for real-time processing of ECAP in closed-loop ENS systems. </div><div><br></div><div>This thesis focuses on developing the DSP architecture for real-time processing of ECAP, including stimulus artifact rejection (SAR), denoising, and extraction of nerve fiber responses as biomedical features, and its VLSI implementation for optimal hardware costs. The first part presents the DSP architecture for real-time SAR and denoising of ECAP in NRT systems. A bidirectional-filtered coherent averaging (BFCA) method is proposed, which enables the configurable linear-phase filter to be realized hardware efficiently for distortion-free filtering of ECAPs and can be easily combined with the alternating-polarity (AP) stimulation method for SAR. Design techniques including folded-IIR filter and division-free averaging are incorporated to reduce the computation cost. The second part presents the fiber-response extraction engine (FREE), a dedicated DSP engine for nerve activation control in the ANC platform. FREE employs the DSP architecture of the BFCA method combined with the AP stimulation, and the architecture of computationally efficient peak detection and classification algorithms for fiber response extraction from ECAP. FREE is mapped onto a custom-made and battery-powered wearable wireless device incorporating a low-power FPGA, a Bluetooth transceiver, a stimulation and recording analog front-end and a power-management unit. In comparison with previous software-based signal processing, FREE not only reduces the data rate of wireless devices but also improves the precision of fiber response classification in noisy environments, which contributes to the construction of high-accuracy nerve activation profile in the ANC platform. An application-specific integrated circuit (ASIC) version of FREE is implemented in 180-nm CMOS technology, with total chip area and core power consumption of 19.98 mm² and 1.95 mW, respectively. </div><div><br></div>

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