05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Nontraditional design of dynamic logics using FDSOI for ultra-efficient computing(2023) Kumar, Shubham; Chatterjee, Swetaki; Dabhi, Chetan Kumar; Chauhan, Yogesh Singh; Amrouch, HussamItem Open Access Review on resistive switching devices based on multiferroic BiFeO3(2023) Zhao, Xianyue; Menzel, Stephan; Polian, Ilia; Schmidt, Heidemarie; Du, NanThis review provides a comprehensive examination of the state-of-the-art research on resistive switching (RS) in BiFeO3 (BFO)-based memristive devices. By exploring possible fabrication techniques for preparing the functional BFO layers in memristive devices, the constructed lattice systems and corresponding crystal types responsible for RS behaviors in BFO-based memristive devices are analyzed. The physical mechanisms underlying RS in BFO-based memristive devices, i.e., ferroelectricity and valence change memory, are thoroughly reviewed, and the impact of various effects such as the doping effect, especially in the BFO layer, is evaluated. Finally, this review provides the applications of BFO devices and discusses the valid criteria for evaluating the energy consumption in RS and potential optimization techniques for memristive devices.Item Open Access Cryogenic embedded system to support quantum computing : from 5-nm FinFET to full processor(2023) Genssler, Paul R.; Klemme, Florian; Parihar, Shivendra Singh; Brandhofer, Sebastian; Pahwa, Girish; Polian, Ilia; Chauhan, Yogesh Singh; Amrouch, HussamItem Open Access Quantum support vector machines of high-dimensional data for image classification problems(2023) Vikas Singh, RajputThis 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.Item Open Access Cryogenic in-memory computing for quantum processors using commercial 5-nm FinFETs(2023) Parihar, Shivendra Singh; Thomann, Simon; Pahwa, Girish; Chauhan, Yogesh Singh; Amrouch, HussamItem Open Access A complete design-for-test scheme for reconfigurable scan networks(2023) Lylina, Natalia; Wang, Chih-Hao; Wunderlich, Hans-JoachimReconfigurable Scan Networks (RSNs) are widely used for accessing instruments offline during debug, test and validation, as well as for performing system-level-test and online system health monitoring. The correct operation of RSNs is essential, and RSNs have to be thoroughly tested. However, due to their inherently sequential structure and complex control dependencies, large parts of RSNs have limited observability and controllability. As a result, certain faults at the interfaces to the instruments, control primitives and scan segments remain undetected by existing test methods. In the paper at hand, Design-for-test (DfT) schemes are developed to overcome the testability problems e.g. by resynthesizing the initial design. A DfT scheme for RSNs is presented, which allows detecting all single stuck-at-faults in RSNs by using existing test generation techniques. The developed scheme analyzes and ensures the testability of all parts of RSNs, which include scan segments, control primitives, and interfaces to the instruments. Therefore, the developed scheme is referred to as a complete DfT scheme . It allows for a test integration to cover multiple fault locations can with a single efficient test sequence and to reduce overall test cost.Item Open Access Multi-material blind beam hardening correction in near real-time based on non-linearity adjustment of projections(2023) Alsaffar, Ammar; Sun, Kaicong; Simon, SvenBeam hardening (BH) is one of the major artifacts that severely reduces the quality of computed tomography (CT) imaging. This BH artifact arises due to the polychromatic nature of the X-ray source and causes cupping and streak artifacts. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. This is achieved by performing a wide sweep of the material based on an experimentally measured look-up table to obtain the closest estimate of the material. Then, the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated polychromatic projection is accurately derived using the least square estimation (LSE) method by minimizing the difference between the experimental projection and the linear combination of simulated polychromatic projections. As a result, an accurate non-linearity correction term is derived that leads to an accurate BH correction result. The simulated projections in this work are performed using a multi-GPU-accelerated forward projection model which ensures a fast BH correction in near real-time. To evaluate the proposed BH correction method, we have conducted extensive experiments on real-world CT data. It is shown that the proposed method results in images with improved contrast-to-noise ratio (CNR) in comparison to the images corrected from only the scatter artifacts and the BH-corrected images using the state-of-the-art empirical BH correction method.Item Open Access Identifying resistive open defects in embedded cells under variations(2023) Najafi-Haghi, Zahra Paria; Wunderlich, Hans-JoachimSmall Delay Faults (SDFs) due to weak defects and marginalities have to be distinguished from extra delays due to process variations, since they may form a reliability threat even if the resulting timing is within the specification. In this paper, it is shown that these faults can still be identified, even if the corresponding defect cell is deeply embedded into a combinational circuit and its observability is restricted. The results of a few delay tests at different voltages and frequencies serve as the input to machine learning procedures which can classify a circuit as marginal due to defects or just slow due to variations. Several machine learning techniques are investigated and compared with respect to accuracy, precision, and recall for different circuit sizes and defect scales. The classification strategies are powerful enough to sort out defective devices without a major impact on yield.Item Open Access Systematic construction of deadlock-free routing for NoC using integer linear programming(2023) Liu, Shuang; Radetzki, MartinNetwork-on-Chip (NoC) presents a promising solution for on-chip communication in highly integrated System-on-Chips (SoCs). This work addresses critical challenges in NoC design, including routing construction, application mapping, and particularly the issue of deadlocks in the widely-used wormhole routing method. In this paper, an Integer Linear Programming (ILP) approach for deadlock-free routing is proposed, applicable to arbitrary network topologies. We systematically analyze deadlock-free routing construction for mesh and torus topologies under uniform random traffic and provide alternative solutions to turn models. In the context of application-specific NoCs, application mapping, and deadlock-free routing are integrated within a single ILP. Through evaluation with several benchmark applications, it is demonstrated that the ILP method consistently delivers optimal solutions and could obtain better results than various heuristic methods within an acceptable time. Fault tolerance is also explored and existing techniques are incorporated into the ILP approach. As an illustrative example, application mapping and a 1-link-fault-tolerant deadlock-free routing for the MP3 application on a mesh network is performed.Item Open Access Cervical muscle reflexes during lateral accelerations(2023) Millard, Matthew; Hunger, Susanne; Broß, Lisa; Fehr, Jörg; Holzapfel, Christian; Stutzig, Norman; Siebert, TobiasAutonomous vehicles will allow a variety of seating orientations that may change the risk of neck injury during an accident. Having a rotated head at the time of a rear-end collision in a conventional vehicle is associated with a higher risk of acute and chronic whiplash. The change in posture affects both the movement of the head and the response of the muscles. We are studying the reflexes of the muscles of the neck so that we can validate the responses of digital human body models that are used in crash simulations. The neck movements and muscle activity of 21 participants (11 female) were recorded at the Stuttgart FKFS mechanical driving simulator. During the maneuver we recorded the acceleration of the seat and electromyographic (EMG) signals from the sternocleidomastoid (STR) muscles using a Biopac MP 160 system (USA). As intuition would suggest, the reflexes of the muscles of the neck are sensitive to posture and the direction of the acceleration.