Vertical stacking—based on modern manufacturing and integration technologies—of multiple 2D chips enables three-dimensional integrated circuits (3D ICs). This exploitation of the third dimension is generally accepted for aiming at higher packing densities, heterogeneous integration, shorter interconnects, reduced power consumption, increased data bandwidth, and realizing highly-parallel systems in one device. However, the commercial acceptance of 3D ICs is currently behind its expectations, mainly due to challenges regarding manufacturing and integration technologies as well as design automation. This work addresses three selected, practically relevant design challenges: (i) increasing the constrained reusability of proven, reliable 2D intellectual property blocks, (ii) planning different types of (comparatively large) through-silicon vias with focus on their impact on design quality, as well as (iii) structural planning of massively-parallel, 3D-IC-specific interconnect structures during 3D floorplanning. A key concept of this work is to account for interconnect structures and their properties during early design phases in order to support effective and high-quality 3D-IC-design flows. To tackle the above listed challenges, modular design-flow extensions and methodologies have been developed. Experimental investigations reveal the effectiveness and efficiency of the proposed techniques, and provide findings on 3D integration with particular focus on interconnect structures. We suggest consideration of these findings when formulating guidelines for successful 3D-IC design automation. / Dreidimensional integrierte Schaltkreise (3D-ICs) beruhen auf neuartigen Herstellungs- und Integrationstechnologien, wobei vor allem “klassische” 2D-ICs vertikal zu einem neuartigen 3D-System gestapelt werden. Dieser Ansatz zur Erschließung der dritten Dimension im Schaltkreisentwurf ist nach Expertenmeinung dazu geeignet, höhere Integrationsdichten zu erreichen, heterogene Integration zu realisieren, kürzere Verdrahtungswege zu ermöglichen, Leistungsaufnahmen zu reduzieren, Datenübertragungsraten zu erhöhen, sowie hoch-parallele Systeme in einer Baugruppe umzusetzen. Aufgrund von technologischen und entwurfsmethodischen Schwierigkeiten bleibt jedoch bisher die kommerzielle Anwendung von 3D-ICs deutlich hinter den Erwartungen zurück. In dieser Arbeit werden drei ausgewählte, praktisch relevante Problemstellungen der Entwurfsautomatisierung von 3D-ICs bearbeitet: (i) die Verbesserung der (eingeschränkten) Wiederverwendbarkeit von zuverlässigen 2D-Intellectual-Property-Blöcken, (ii) die komplexe Planung von verschiedenartigen, verhältnismäßig großen Through-Silicion Vias unter Beachtung ihres Einflusses auf die Entwurfsqualität, und (iii) die strukturelle Einbindung von massiv-parallelen, 3D-IC-spezifischen Verbindungsstrukturen während der Floorplanning-Phase. Das Ziel dieser Arbeit besteht darin, Verbindungsstrukturen mit deren wesentlichen Eigenschaften bereits in den frühen Phasen des Entwurfsprozesses zu berücksichtigen. Dies begünstigt einen qualitativ hochwertigen Entwurf von 3D-ICs. Die in dieser Arbeit vorgestellten modularen Entwurfsprozess-Erweiterungen bzw. -Methodiken dienen zur effizienten Lösung der oben genannten Problemstellungen. Experimentelle Untersuchungen bestätigen die Wirksamkeit sowie die Effektivität der erarbeiten Methoden. Darüber hinaus liefern sie praktische Erkenntnisse bezüglich der Anwendung von 3D-ICs und der Planung deren Verbindungsstrukturen. Diese Erkenntnisse sind zur Ableitung von Richtlinien für den erfolgreichen Entwurf von 3D-ICs dienlich.

Vertical stacking—based on modern manufacturing and integration technologies—of multiple 2D chips enables three-dimensional integrated circuits (3D ICs). This exploitation of the third dimension is generally accepted for aiming at higher packing densities, heterogeneous integration, shorter interconnects, reduced power consumption, increased data bandwidth, and realizing highly-parallel systems in one device. However, the commercial acceptance of 3D ICs is currently behind its expectations, mainly due to challenges regarding manufacturing and integration technologies as well as design automation. This work addresses three selected, practically relevant design challenges: (i) increasing the constrained reusability of proven, reliable 2D intellectual property blocks, (ii) planning different types of (comparatively large) through-silicon vias with focus on their impact on design quality, as well as (iii) structural planning of massively-parallel, 3D-IC-specific interconnect structures during 3D floorplanning. A key concept of this work is to account for interconnect structures and their properties during early design phases in order to support effective and high-quality 3D-IC-design flows. To tackle the above listed challenges, modular design-flow extensions and methodologies have been developed. Experimental investigations reveal the effectiveness and efficiency of the proposed techniques, and provide findings on 3D integration with particular focus on interconnect structures. We suggest consideration of these findings when formulating guidelines for successful 3D-IC design automation.:1 Introduction 1.1 The 3D Integration Approach for Electronic Circuits 1.2 Technologies for 3D Integrated Circuits 1.3 Design Approaches for 3D Integrated Circuits 2 State of the Art in Design Automation for 3D Integrated Circuits 2.1 Thermal Management 2.2 Partitioning and Floorplanning 2.3 Placement and Routing 2.4 Power and Clock Delivery 2.5 Design Challenges 3 Research Objectives 4 Planning Through-Silicon Via Islands for Block-Level Design Reuse 4.1 Problems for Design Reuse in 3D Integrated Circuits 4.2 Connecting Blocks Using Through-Silicon Via Islands 4.2.1 Problem Formulation and Methodology Overview 4.2.2 Net Clustering 4.2.3 Insertion of Through-Silicon Via Islands 4.2.4 Deadspace Insertion and Redistribution 4.3 Experimental Investigation 4.3.1 Wirelength Estimation 4.3.2 Configuration 4.3.3 Results and Discussion 4.4 Summary and Conclusions 5 Planning Through-Silicon Vias for Design Optimization 5.1 Deadspace Requirements for Optimized Planning of Through-Silicon Vias 5.2 Multiobjective Design Optimization of 3D Integrated Circuits 5.2.1 Methodology Overview and Configuration 5.2.2 Techniques for Deadspace Optimization 5.2.3 Design-Quality Analysis 5.2.4 Planning Different Types of Through-Silicon Vias 5.3 Experimental Investigation 5.3.1 Configuration 5.3.2 Results and Discussion 5.4 Summary and Conclusions 6 3D Floorplanning for Structural Planning of Massive Interconnects 6.1 Block Alignment for Interconnects Planning in 3D Integrated Circuits 6.2 Corner Block List Extended for Block Alignment 6.2.1 Alignment Encoding 6.2.2 Layout Generation: Block Placement and Alignment 6.3 3D Floorplanning Methodology 6.3.1 Optimization Criteria and Phases and Related Cost Models 6.3.2 Fast Thermal Analysis 6.3.3 Layout Operations 6.3.4 Adaptive Optimization Schedule 6.4 Experimental Investigation 6.4.1 Configuration 6.4.2 Results and Discussion 6.5 Summary and Conclusions 7 Research Summary, Conclusions, and Outlook Dissertation Theses Notation Glossary Bibliography / Dreidimensional integrierte Schaltkreise (3D-ICs) beruhen auf neuartigen Herstellungs- und Integrationstechnologien, wobei vor allem “klassische” 2D-ICs vertikal zu einem neuartigen 3D-System gestapelt werden. Dieser Ansatz zur Erschließung der dritten Dimension im Schaltkreisentwurf ist nach Expertenmeinung dazu geeignet, höhere Integrationsdichten zu erreichen, heterogene Integration zu realisieren, kürzere Verdrahtungswege zu ermöglichen, Leistungsaufnahmen zu reduzieren, Datenübertragungsraten zu erhöhen, sowie hoch-parallele Systeme in einer Baugruppe umzusetzen. Aufgrund von technologischen und entwurfsmethodischen Schwierigkeiten bleibt jedoch bisher die kommerzielle Anwendung von 3D-ICs deutlich hinter den Erwartungen zurück. In dieser Arbeit werden drei ausgewählte, praktisch relevante Problemstellungen der Entwurfsautomatisierung von 3D-ICs bearbeitet: (i) die Verbesserung der (eingeschränkten) Wiederverwendbarkeit von zuverlässigen 2D-Intellectual-Property-Blöcken, (ii) die komplexe Planung von verschiedenartigen, verhältnismäßig großen Through-Silicion Vias unter Beachtung ihres Einflusses auf die Entwurfsqualität, und (iii) die strukturelle Einbindung von massiv-parallelen, 3D-IC-spezifischen Verbindungsstrukturen während der Floorplanning-Phase. Das Ziel dieser Arbeit besteht darin, Verbindungsstrukturen mit deren wesentlichen Eigenschaften bereits in den frühen Phasen des Entwurfsprozesses zu berücksichtigen. Dies begünstigt einen qualitativ hochwertigen Entwurf von 3D-ICs. Die in dieser Arbeit vorgestellten modularen Entwurfsprozess-Erweiterungen bzw. -Methodiken dienen zur effizienten Lösung der oben genannten Problemstellungen. Experimentelle Untersuchungen bestätigen die Wirksamkeit sowie die Effektivität der erarbeiten Methoden. Darüber hinaus liefern sie praktische Erkenntnisse bezüglich der Anwendung von 3D-ICs und der Planung deren Verbindungsstrukturen. Diese Erkenntnisse sind zur Ableitung von Richtlinien für den erfolgreichen Entwurf von 3D-ICs dienlich.:1 Introduction 1.1 The 3D Integration Approach for Electronic Circuits 1.2 Technologies for 3D Integrated Circuits 1.3 Design Approaches for 3D Integrated Circuits 2 State of the Art in Design Automation for 3D Integrated Circuits 2.1 Thermal Management 2.2 Partitioning and Floorplanning 2.3 Placement and Routing 2.4 Power and Clock Delivery 2.5 Design Challenges 3 Research Objectives 4 Planning Through-Silicon Via Islands for Block-Level Design Reuse 4.1 Problems for Design Reuse in 3D Integrated Circuits 4.2 Connecting Blocks Using Through-Silicon Via Islands 4.2.1 Problem Formulation and Methodology Overview 4.2.2 Net Clustering 4.2.3 Insertion of Through-Silicon Via Islands 4.2.4 Deadspace Insertion and Redistribution 4.3 Experimental Investigation 4.3.1 Wirelength Estimation 4.3.2 Configuration 4.3.3 Results and Discussion 4.4 Summary and Conclusions 5 Planning Through-Silicon Vias for Design Optimization 5.1 Deadspace Requirements for Optimized Planning of Through-Silicon Vias 5.2 Multiobjective Design Optimization of 3D Integrated Circuits 5.2.1 Methodology Overview and Configuration 5.2.2 Techniques for Deadspace Optimization 5.2.3 Design-Quality Analysis 5.2.4 Planning Different Types of Through-Silicon Vias 5.3 Experimental Investigation 5.3.1 Configuration 5.3.2 Results and Discussion 5.4 Summary and Conclusions 6 3D Floorplanning for Structural Planning of Massive Interconnects 6.1 Block Alignment for Interconnects Planning in 3D Integrated Circuits 6.2 Corner Block List Extended for Block Alignment 6.2.1 Alignment Encoding 6.2.2 Layout Generation: Block Placement and Alignment 6.3 3D Floorplanning Methodology 6.3.1 Optimization Criteria and Phases and Related Cost Models 6.3.2 Fast Thermal Analysis 6.3.3 Layout Operations 6.3.4 Adaptive Optimization Schedule 6.4 Experimental Investigation 6.4.1 Configuration 6.4.2 Results and Discussion 6.5 Summary and Conclusions 7 Research Summary, Conclusions, and Outlook Dissertation Theses Notation Glossary Bibliography

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

A digital system is often required to operate under a specific frequency. A ring oscillator can be helpful in this circumstance because it can generate a signal with a specific frequency. However, a ring oscillator is also sensitive to the environment temperature. With the increasing requirement of accuracy and stability, many approaches appear worldwide to make a temperature-insensitive ring oscillator. This thesis project presents an approach to compensate the temperature effect on a Current Starved Ring Oscillator(CSRO). More concretely, we researched how to achieve temperature compensation for CSRO in a digitally-controlled configuration. A Phase Frequency Detector (PFD) block is adapted to sense the frequency difference between the reference frequency and CSRO frequency. Two Charge Pumps (CP)are used to quantify the difference in voltage signal. A Dynamic Comparator block compares the signals from CPs. A following Bidirectional Counter block can count up or down to change the current in CSRO by a four-bit signal. In the end, the CSRO can generate an oscillating signal at the appropriate frequency after some adaptation time. This proposed circuit was realized with AMS 0.35 um CMOS technology and simulated using the Cadence tools. Power consumption, temperature compensation analysis and voltage supply compensation analysis under different temperatures are also performed in the project.

In this masters thesis we focused on using machine learning methods for detecting money laundering in financial transaction networks, in order to demonstrate that it can be used as a complement or instead of the more commonly used rule based systems. The graph learning method graph convolutional networks (GCN) has been a hot topic in the field since they were shown to scale well with data size back in 2018. However the typical GCN models cannot use edge features, which is why this thesis combines the GCN model with a node and edge neural network (NENN) in order to solve this problem. This new method will be compared towards an already established machine learning method for financial transactions, namely the tree boosting method (XGBoost). Because of confidentiality concerns for financial transactions data, the machine learning algorithms will be tested on two carefully constructed synthetically generated data sets, which from agent based simulations resembles real financial data. The results showed the viability and superiority of the new implementation of the GCN model with it being a preferable method for connectivly structured data, meaning that a transaction or account is analyzed in the context of its financial environment. On the other hand the XGBoost method showed better results when examining transactions independently. Hence it was more accurately able to find fraudulent and non fraudulent patterns from the transactional features themselves. / I detta examensarbete fokuserar vi på användandet av maskininlärningsmetoder för att detektera penningtvätt i finansiella transaktionsnätverk, med målet att demonstrera att dess kan användas som ett komplement till eller i stället för de mer vanligt använda regelbaserade systemen. Grafinlärningsmetoden \textit{graph convolutional networks} (GCN) som har varit ett hett ämne inom området sedan metoden under 2018 visades fungera bra för stora datamängder. Däremot kan inte en vanlig GCN-modell använda kantinformation, vilket är varför denna avhandling kombinerar GCN-modellen med \textit{node and edge neural networks} (NENN) för att mer effektivt detektera penningtvätt. Denna nya metod kommer att jämföras med en redan etablerad maskininlärningsmetod för finansiella transaktioner, nämligen \textit{tree boosting} (XGBoost). På grund av sekretessanledningar för finansiella transaktionsdata var maskininlärningsalgoritmerna testade på två noggrant konstruerade syntetiskt genererade datamängder som från agentbaserade simuleringar liknar riktiga finansiella data. Resultaten visade på applikationsmöjligheter och överlägsenhet för den nya implementationen av GCN-modellen vilken är att föredra för relationsstrukturerade data, det vill säga när transaktioner och konton analyseras i kontexten av deras finansiella omgivning. Å andra sidan visar XGBoost bättre resultat på att examinera transaktioner individuellt eftersom denna metod mer precist kan identifiera bedrägliga och icke-bedrägliga mönster från de transnationella funktionerna.

As outer space becomes more commercialized, humans have invested more time and efforts into the use and exploration of the outer space domain. Captured by the display of opportunities, space is becoming increasingly overcrowded. Currently, the only framework fully embracing the exploration and use of outer space is the Outer Space Treaty (OST), put forward by the United Nations in 1967. While the OST is detailing how space faring nations ought to peacefully use outer space as a joint arena for technological development, the discourse on outer space governance still experience some serious definitional limitations. This paper examines two interconnected deficiencies in the literature on outer space; first, the insufficient research and development of a space power theory and; second, the absence of, and the challenges in constructing and all- encompassing definition and/or delimitation of outer space. To support the discussion and to reach some definitive conclusions of the current status of these issues, a selection of historical theories and contemporary cases have been applied to enrich the debate. By identifying gaps in the literature, this paper strives to highlight some of the controversies in the outer space discourse.

One of the major applications of wireless sensor networks is to sense accurate and reliable data from the physical environment with or without a priori knowledge of data statistics. To extract accurate data from the physical environment, we investigate spatial data correlation among sensor nodes to develop data accuracy models. We propose three data accuracy models namely Estimated Data Accuracy (EDA) model, Cluster based Data Accuracy (CDA) model and Distributed Cluster based Data Accuracy (DCDA) model with a priori knowledge of data statistics. Due to the deployment of high density of sensor nodes, observed data are highly correlated among sensor nodes which form distributed clusters in space. We describe two clustering algorithms called Deterministic Distributed Clustering (DDC) algorithm and Spatial Data Correlation based Distributed Clustering (SDCDC) algorithm implemented under CDA model and DCDA model respectively. Moreover, due to data correlation in the network, it has redundancy in data collected by sensor nodes. Hence, it is not necessary for all sensor nodes to transmit their highly correlated data to the central node (sink node or cluster head node). Even an optimal set of sensor nodes are capable of measuring accurate data and transmitting the accurate, precise data to the central node. This reduces data redundancy, energy consumption and data transmission cost to increase the lifetime of sensor networks. Finally, we propose a fourth accuracy model called Adaptive Data Accuracy (ADA) model that doesn't require any a priori knowledge of data statistics. ADA model can sense continuous data stream at regular time intervals to estimate accurate data from the environment and select an optimal set of sensor nodes for data transmission to the network. Data transmission can be further reduced for these optimal sensor nodes by transmitting a subset of sensor data using a methodology called Spatio-Temporal Data Prediction (STDP) model under data reduction strategies. Furthermore, we implement data accuracy model when the network is under a threat of malicious attack.

Two distinct overall research objectives are in this Masters thesis. Very little relates the two chapters apart from the ligands. The first chapter addresses diastereoselective homogeneous copper catalyzed cyclopropanation reactions. Cyclopropanation of styrene and ethyl diazoacetate (EDA) is a standard test reaction for homogeneous catalysts. Sterically bulky salicylaldimine (SAL) ligands should select for the ethyl trans-2-phenylcyclopropanecarboxylate diastereomer. Steric bulk poorly influences trans:cis ratios. Salicylaldiminine ligands do not posses the correct symmetry to affect diastereoselectivity. The SAL ligand belongs to the Cs point group in the solid state. Other ligand motifs are more effective at altering the trans:cis ratios. The second chapter addresses the general route toward successful copper(II) ethylene polymerization catalysts. Catalytic activity of the copper(II) complexes is very low. Polymer chain growth from a copper catalyst is very unlikely. Copper-carbon bonds decompose by homolytic cleavage or C-H activation. Copper-alkyls and aryls readily decompose into brown colored oils and salts with different colors. Ligand transfer to trimethylaluminum (TMA) appears to explain low yield ethylene polymerization.

Two distinct overall research objectives are in this Masters thesis. Very little relates the two chapters apart from the ligands. The first chapter addresses diastereoselective homogeneous copper catalyzed cyclopropanation reactions. Cyclopropanation of styrene and ethyl diazoacetate (EDA) is a standard test reaction for homogeneous catalysts. Sterically bulky salicylaldimine (SAL) ligands should select for the ethyl trans-2-phenylcyclopropanecarboxylate diastereomer. Steric bulk poorly influences trans:cis ratios. Salicylaldiminine ligands do not posses the correct symmetry to affect diastereoselectivity. The SAL ligand belongs to the Cs point group in the solid state. Other ligand motifs are more effective at altering the trans:cis ratios. The second chapter addresses the general route toward successful copper(II) ethylene polymerization catalysts. Catalytic activity of the copper(II) complexes is very low. Polymer chain growth from a copper catalyst is very unlikely. Copper-carbon bonds decompose by homolytic cleavage or C-H activation. Copper-alkyls and aryls readily decompose into brown colored oils and salts with different colors. Ligand transfer to trimethylaluminum (TMA) appears to explain low yield ethylene polymerization.