05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Start date: 2020Publication Type: search.filters.UBSTyp.Abschlussarbeit (Master)Institute: search.filters.UBSInstitut.Institut für Technische Informatik

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    Development of an infrastructure for creating a behavioral model of hardware of measurable parameters in dependency of executed software
    (2021) Schwachhofer, Denis
    System-Level Test (SLT) gains traction not only in the industry but as of recently also in academia. It is used to detect manufacturing defects not caught by previous test steps. The idea behind SLT is to embed the Design Under Test (DUT) in an environment and running software on it that corresponds to its end-user application. But even though it is increasingly used in manufacturing since a decade there are still many open challenges to solve. For example, there is no coverage metric for SLT. Also, tests are not automatically generated but manually composed using existing operating systems and programs. This master thesis introduces the foundation for the AutoGen project, that will tackle the aforementioned challenges in the future. This foundation contains a platform for experiments and a workflow to generate Systems-on-Chip (SoCs). A case study is conducted to show an example on how on-chip sensors can be used in SLT applications to replace missing detailed technology-information. For the case study a “power devil” application has been developed that aims to keep the temperature of the Field Programmable Gate Array (FPGA) it runs on in a target range. The study shows an example on how software and parameters influence the extra-functional behavior of hardware.
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    Quantum support vector machines of high-dimensional data for image classification problems
    (2023) Vikas Singh, Rajput
    This thesis presents a comprehensive investigation into the efficient utilization of Quantum Support Vector Machines (QSVMs) for image classification on high-dimensional data. The primary focus is on analyzing the standard MNIST dataset and the high-dimensional dataset provided by TRUMPF SE + Co. KG. To evaluate the performance of QSVMs against classical Support Vector Machines (SVMs) for high-dimensional data, a benchmarking framework is proposed. In the current Noisy Intermediate Scale Quantum (NISQ) era, classical preprocessing of the data is a crucial step to prepare the data for classification tasks using NISQ machines. Various dimensionality reduction techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and convolutional autoencoders, are explored to preprocess the image datasets. Convolutional autoencoders are found to outperform other methods when calculating quantum kernels on a small dataset. Furthermore, the benchmarking framework systematically analyzes different quantum feature maps by varying hyperparameters, such as the number of qubits, the use of parameterized gates, the number of features encoded per qubit line, and the use of entanglement. Quantum feature maps demonstrate higher accuracy compared to classical feature maps for both TRUMPF and MNIST data. Among the feature maps, one using 𝑅𝑧 and 𝑅𝑦 gates with two features per qubit, without entanglement, achieves the highest accuracy. The study also reveals that increasing the number of qubits leads to improved accuracy for the real-world TRUMPF dataset. Additionally, the choice of the quantum kernel function significantly impacts classification results, with the projected type quantum kernel outperforming the fidelity type quantum kernel. Subsequently, the study examines the Kernel Target Alignment (KTA) optimization method to improve the pipeline. However, for the chosen feature map and dataset, KTA does not provide significant benefits. In summary, the results highlight the potential for achieving quantum advantage by optimizing all components of the quantum classifier framework. Selecting appropriate dimensionality reduction techniques, quantum feature maps, and quantum kernel methods is crucial for enhancing classification accuracy. Further research is needed to address challenges related to kernel optimization and fully leverage the capabilities of quantum computing in machine learning applications.