Microwave Photonic Applications - From Chip Level to System Level

Die Vermischung von Mikrowellen- und optischen Technologien – Mikrowellenphotonik – ist ein neu aufkommendes Feld mit hohem Potential. Durch die Nutzung der Vorzüge beider Welten hat die Mikrowellenphotonik viele Anwendungsfälle und ist gerade erst am Beginn ihrer Erfolgsgeschichte. Der Weg für neue Konzepte, neue Komponenten und neue Anwendungen wird dadurch geebnet, dass ein höherer Grad an Integration sowie neue Technologien wie Silicon Photonics verfügbar sind. In diesem Werk werden zuerst die notwendigen grundlegenden Basiskomponenten – optische Quelle, elektro-optische Wandlung, Übertragungsmedium und opto-elektrische Wandlung – eingeführt. Mithilfe spezifischer Anwendungsbeispiele, die von Chipebene bis hin zur Systemebene reichen, wird der elektrooptische Codesign-Prozess veranschaulicht. Schließlich werden zukünftige Ausrichtungen wie die Unterstützung von elektrischen Trägern im Millimeterwellen- und THz-Bereich sowie Realisierungsoptionen in integrierter Optik und Nanophotonik diskutiert.The hybridization between microwave and optical technologies – microwave photonics – is an emerging field with high potential. Benefitting from the best of both worlds, microwave photonics has many use cases and is just at the beginning of its success story. The availability of a higher degree of integration and new technologies such as silicon photonics paves the way for new concepts, new components and new applications. In this work, first, the necessary basic building blocks – optical source, electro-optical conversion, transmission medium and opto-electrical conversion – are introduced. With the help of specific application examples ranging from chip level to system level, the electro-optical co-design process for microwave photonic systems is illustrated. Finally, future directions such as the support of electrical carriers in the millimeter wave and THz range and realization options in integrated optics and nanophotonics are discussed

On thermal sensor calibration and software techniques for many-core thermal management

The high power density of a many-core processor results in increased temperature which negatively impacts system reliability and performance. Dynamic thermal management applies thermal-aware techniques at run time to avoid overheating using temperature information collected from on-chip thermal sensors. Temperature sensing and thermal control schemes are two critical technologies for successfully maintaining thermal safety. In this dissertation, on-line thermal sensor calibration schemes are developed to provide accurate temperature information. Software-based dynamic thermal management techniques are proposed using calibrated thermal sensors. Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration cost for thermal sensors can be prohibitively high as the number of on-chip sensors increases. Linear models which are suitable for on-line calculation are employed to estimate temperatures at multiple sensor locations using performance counters. The estimated temperature and the actual sensor thermal profile show a very high similarity with correlation coefficient ~0.9 for SPLASH2 and SPEC2000 benchmarks. A calibration approach is proposed to combine potentially inaccurate temperature values obtained from two sources: thermal sensor readings and temperature estimations. A data fusion strategy based on Bayesian inference, which combines information from these two sources, is demonstrated. The result shows the strategy can effectively recalibrate sensor readings in response to inaccuracies caused by process variation and environmental noise. The average absolute error of the corrected sensor temperature readings is A dynamic task allocation strategy is proposed to address localized overheating in many-core systems. Our approach employs reinforcement learning, a dynamic machine learning algorithm that performs task allocation based on current temperatures and a prediction regarding which assignment will minimize the peak temperature. Our results show that the proposed technique is fast (scheduling performed in \u3c1 \u3ems) and can efficiently reduce peak temperature by up to 8 degree C in a 49-core processor (6% on average) versus a leading competing task allocation approach for a series of SPLASH-2 benchmarks. Reinforcement learning has also been applied to 3D integrated circuits to allocate tasks with thermal awareness

Flight Avionics Hardware Roadmap

As part of NASA's Avionics Steering Committee's stated goal to advance the avionics discipline ahead of program and project needs, the committee initiated a multi-Center technology roadmapping activity to create a comprehensive avionics roadmap. The roadmap is intended to strategically guide avionics technology development to effectively meet future NASA missions needs. The scope of the roadmap aligns with the twelve avionics elements defined in the ASC charter, but is subdivided into the following five areas: Foundational Technology (including devices and components), Command and Data Handling, Spaceflight Instrumentation, Communication and Tracking, and Human Interfaces

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy