@inbook{62067,
  abstract     = {{Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and error-prone because the whole network stack and communication patterns have to be implemented and managed on the FPGAs. Also, this approach conceptually involves a trade-off between the performance potential of improved communication and the impact of resource consumption for communication infrastructure, since the utilized resources on the FPGAs could otherwise be used for computations. In this work, we investigate this trade-off, firstly, by using synthetic benchmarks to evaluate the different configuration options of the communication framework ACCL and their impact on communication latency and throughput. Finally, we use our findings to implement a shallow water simulation whose scalability heavily depends on low-latency communication. With a suitable configuration of ACCL, good scaling behavior can be shown to all 48 FPGAs installed in the system. Overall, the results show that the availability of inter-FPGA communication frameworks as well as the configurability of framework and network stack are crucial to achieve the best application performance with low latency communication.}},
  author       = {{Meyer, Marius and Kenter, Tobias and Petrica, Lucian and O’Brien, Kenneth and Blott, Michaela and Plessl, Christian}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783031697654}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Optimizing Communication for Latency Sensitive HPC Applications on up to 48 FPGAs Using ACCL}}},
  doi          = {{10.1007/978-3-031-69766-1_9}},
  year         = {{2024}},
}

@article{56604,
  abstract     = {{This manuscript makes the claim of having computed the 9th Dedekind number, D(9). This was done by accelerating the core operation of the process with an efficient FPGA design that outperforms an optimized 64-core CPU reference by 95x. The FPGA execution was parallelized on the Noctua 2 supercomputer at Paderborn University. The resulting value for D(9) is 286386577668298411128469151667598498812366. This value can be verified in two steps. We have made the data file containing the 490 M results available, each of which can be verified separately on CPU, and the whole file sums to our proposed value. The paper explains the mathematical approach in the first part, before putting the focus on a deep dive into the FPGA accelerator implementation followed by a performance analysis. The FPGA implementation was done in Register-Transfer Level using a dual-clock architecture and shows how we achieved an impressive FMax of 450 MHz on the targeted Stratix 10 GX 2,800 FPGAs. The total compute time used was 47,000 FPGA hours.}},
  author       = {{Van Hirtum, Lennart and De Causmaecker, Patrick and Goemaere, Jens and Kenter, Tobias and Riebler, Heinrich and Lass, Michael and Plessl, Christian}},
  issn         = {{1936-7406}},
  journal      = {{ACM Transactions on Reconfigurable Technology and Systems}},
  number       = {{3}},
  pages        = {{1--28}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{A Computation of the Ninth Dedekind Number Using FPGA Supercomputing}}},
  doi          = {{10.1145/3674147}},
  volume       = {{17}},
  year         = {{2024}},
}

@inproceedings{53503,
  author       = {{Olgu, Kaan and Kenter, Tobias and Nunez-Yanez, Jose and Mcintosh-Smith, Simon}},
  booktitle    = {{Proceedings of the 12th International Workshop on OpenCL and SYCL}},
  publisher    = {{ACM}},
  title        = {{{Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures}}},
  doi          = {{10.1145/3648115.3648134}},
  year         = {{2024}},
}

@inbook{57834,
  author       = {{Vernholz, Mats}},
  booktitle    = {{Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2024}},
  editor       = {{Kögler, Kristina and Kremer, H.-Hugo and Herkner, Volkmar}},
  pages        = {{132--147}},
  publisher    = {{Verlag Barbara Budrich}},
  title        = {{{Gewerblich-technische Lehrkräftebildung in Deutschland - Analyse der Einflüsse auf das akademische Selbstkonzept von Lehramtsstudierenden technischer (beruflicher) Fachrichtungen}}},
  doi          = {{10.3224/84743054}},
  year         = {{2024}},
}

@inbook{53942,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Since its inception two decades ago, <jats:sc>Soot</jats:sc> has become one of the most widely used open-source static analysis frameworks. Over time it has been extended with the contributions of countless researchers. Yet, at the same time, the requirements for <jats:sc>Soot</jats:sc> have changed over the years and become increasingly at odds with some of the major design decisions that underlie it. In this work, we thus present <jats:sc>SootUp</jats:sc>, a complete reimplementation of <jats:sc>Soot</jats:sc> that seeks to fulfill these requirements with a novel design, while at the same time keeping elements that <jats:sc>Soot</jats:sc> users have grown accustomed to.</jats:p>}},
  author       = {{Karakaya, Kadiray and Schott, Stefan and Klauke, Jonas and Bodden, Eric and Schmidt, Markus and Luo, Linghui and He, Dongjie}},
  booktitle    = {{Tools and Algorithms for the Construction and Analysis of Systems}},
  isbn         = {{9783031572456}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{SootUp: A Redesign of the Soot Static Analysis Framework}}},
  doi          = {{10.1007/978-3-031-57246-3_13}},
  year         = {{2024}},
}

@inproceedings{57550,
  author       = {{Schott, Stefan and Ponta, Serena Elisa and Fischer, Wolfram and Klauke, Jonas and Bodden, Eric}},
  booktitle    = {{38th European Conference on Object-Oriented Programming (ECOOP 2024)}},
  location     = {{Vienna}},
  title        = {{{Java Bytecode Normalization for Code Similarity Analysis}}},
  doi          = {{10.4230/LIPIcs.ECOOP.2024.37}},
  year         = {{2024}},
}

@inproceedings{58716,
  author       = {{Schott, Stefan and Fischer, Wolfram and Ponta, Serena Elisa and Klauke, Jonas and Bodden, Eric}},
  booktitle    = {{2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)}},
  publisher    = {{IEEE}},
  title        = {{{Compilation of Commit Changes Within Java Source Code Repositories}}},
  doi          = {{10.1109/icsme58944.2024.00038}},
  year         = {{2024}},
}

@misc{59223,
  author       = {{Schwabe, Tobias and Mallick, Khaleda and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}},
  publisher    = {{Zenodo}},
  title        = {{{Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 }}},
  doi          = {{10.5281/zenodo.15114897}},
  year         = {{2024}},
}

@misc{59224,
  author       = {{Schwabe, Tobias and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}},
  publisher    = {{Zenodo}},
  title        = {{{Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 }}},
  doi          = {{10.5281/zenodo.15114631}},
  year         = {{2024}},
}

@inproceedings{57103,
  author       = {{Surendranath Shroff, Vijayalakshmi and Bahmanian, Meysam and Kruse, Stephan and Scheytt, J. Christoph}},
  booktitle    = {{2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) }},
  location     = {{Fort Lauderdale, Florida}},
  publisher    = {{IEEE}},
  title        = {{{Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology}}},
  doi          = {{10.1109/BCICTS59662.2024.10745663}},
  year         = {{2024}},
}

@inproceedings{57160,
  abstract     = {{Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.}},
  author       = {{Werning, Alexander and Haeb-Umbach, Reinhold}},
  booktitle    = {{32nd European Signal Processing Conference (EUSIPCO 2024)}},
  keywords     = {{data pruning, knowledge distillation, audio tagging}},
  location     = {{Lyon}},
  title        = {{{Target-Specific Dataset Pruning for Compression of Audio Tagging Models}}},
  year         = {{2024}},
}

@inproceedings{56863,
  author       = {{Schiebel, Fabian Benedikt and Sattler, Florian and Schubert, Philipp Dominik and Apel, Sven and Bodden, Eric}},
  booktitle    = {{38th European Conference on Object-Oriented Programming (ECOOP 2024)}},
  editor       = {{Aldrich, Jonathan and Salvaneschi, Guido}},
  isbn         = {{978-3-95977-341-6}},
  issn         = {{1868-8969}},
  pages        = {{36:1–36:28}},
  publisher    = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}},
  title        = {{{Scaling Interprocedural Static Data-Flow Analysis to Large C/C++ Applications: An Experience Report}}},
  doi          = {{10.4230/LIPIcs.ECOOP.2024.36}},
  volume       = {{313}},
  year         = {{2024}},
}

@inbook{61141,
  author       = {{Graf, Lara Marie and Häsel-Weide, Uta and Höveler, K. and Nührenbörger, M.}},
  booktitle    = {{Empirische Sonderpädagogik}},
  pages        = {{334--350}},
  title        = {{{ Typiken zum fachspezifischen Umgang mit Anforderungssituationen diagnosegeleiteter Förderung. Professionalisierung von fachfremd unterrichtenden Lehrkräften zur diagnosegeleiteten Förderung im inklusiven Mathematikunterricht am Beispiel der Förderung von Operationsvorstellungen}}},
  doi          = {{10.2440/003-0036 }},
  year         = {{2024}},
}

@article{63059,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate <jats:italic>dynamic</jats:italic> DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.
</jats:p>}},
  author       = {{Nigade, Vinod and Bauszat, Pablo and Bal, Henri and Wang, Lin}},
  issn         = {{0922-6443}},
  journal      = {{Real-Time Systems}},
  number       = {{2}},
  pages        = {{239--290}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Inference serving with end-to-end latency SLOs over dynamic edge networks}}},
  doi          = {{10.1007/s11241-024-09418-4}},
  volume       = {{60}},
  year         = {{2024}},
}

@article{60195,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>This survey describes the structure of the field of research on curriculum resources in mathematics education in the period from 2018 till 2023. Based on the procedures of a systematic review relevant literature was identified using Web of Science as a database. The included literature was analyzed and categorized according to the type of curriculum resource and the area of study. Seven areas of studies were identified: studies on the role of curriculum resources, content analysis, user studies, studies on the effects of curriculum resources, studies on curriculum resource design, curriculum resources as data, and reviews. The areas were further subdivided into different subcategories based on the research questions of the included papers. The findings show that research on mathematics textbooks is still predominant in the field. The most popular areas of research are content analysis, user studies, studies on design, and studies on effects. Emerging areas are research on students’ use of curriculum resources and the employment of user data from digital curriculum resources as data basis in mathematics education research.</jats:p>}},
  author       = {{Rezat, Sebastian}},
  issn         = {{1863-9690}},
  journal      = {{ZDM – Mathematics Education}},
  number       = {{2}},
  pages        = {{223--237}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Research on curriculum resources in mathematics education: a survey of the field}}},
  doi          = {{10.1007/s11858-024-01559-x}},
  volume       = {{56}},
  year         = {{2024}},
}

@inproceedings{53824,
  author       = {{Koch, Kevin and Claes, Leander and Jurgelucks, Benjamin and Meihost, Lars and Henning, Bernd}},
  booktitle    = {{Fortschritte der Akustik - DAGA 2024}},
  editor       = {{Gesellschaft für Akustik e.V., Deutsche }},
  pages        = {{1113–1116}},
  title        = {{{Inverses Verfahren zur Identifikation piezoelektrischer Materialparameter unterstützt durch neuronale Netze}}},
  year         = {{2024}},
}

@inproceedings{56834,
  author       = {{Friesen, Olga and Claes, Leander and Scheidemann, Claus and Feldmann, Nadine and Hemsel, Tobias and Henning, Bernd}},
  booktitle    = {{2023 International Congress on Ultrasonics, Beijing, China}},
  issn         = {{1742-6596}},
  pages        = {{012125}},
  publisher    = {{IOP Publishing}},
  title        = {{{Estimation of temperature-dependent piezoelectric material parameters using ring-shaped specimens}}},
  doi          = {{10.1088/1742-6596/2822/1/012125}},
  volume       = {{2822}},
  year         = {{2024}},
}

@misc{55470,
  author       = {{Koch, Kevin and Friesen, Olga and Claes, Leander}},
  publisher    = {{Zenodo}},
  title        = {{{Randomised material parameter impedance dataset of piezoelectric rings}}},
  doi          = {{10.5281/zenodo.13143680}},
  year         = {{2024}},
}

@misc{53662,
  author       = {{Koch, Kevin and Claes, Leander}},
  publisher    = {{zenodo}},
  title        = {{{Randomised material parameter piezoelectric impedance dataset with structured electrodes}}},
  doi          = {{10.5281/ZENODO.11064206}},
  year         = {{2024}},
}

@misc{55416,
  author       = {{Claes, Leander and Koch, Kevin and Friesen, Olga and Meihost, Lars}},
  title        = {{{Machine learning in inverse measurement problems: An application to piezoelectric material characterisation}}},
  year         = {{2024}},
}